Browsing by Subject "UAV"

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  • Khoramshahi, Ehsan; Oliveira, Raquel A.; Koivumäki, Niko; Honkavaara, Eija (2020)
    Simultaneous localization and mapping (SLAM) of a monocular projective camera installed on an unmanned aerial vehicle (UAV) is a challenging task in photogrammetry, computer vision, and robotics. This paper presents a novel real-time monocular SLAM solution for UAV applications. It is based on two steps: consecutive construction of the UAV path, and adjacent strip connection. Consecutive construction rapidly estimates the UAV path by sequentially connecting incoming images to a network of connected images. A multilevel pyramid matching is proposed for this step that contains a sub-window matching using high-resolution images. The sub-window matching increases the frequency of tie points by propagating locations of matched sub-windows that leads to a list of high-frequency tie points while keeping the execution time relatively low. A sparse bundle block adjustment (BBA) is employed to optimize the initial path by considering nuisance parameters. System calibration parameters with respect to global navigation satellite system (GNSS) and inertial navigation system (INS) are optionally considered in the BBA model for direct georeferencing. Ground control points and checkpoints are optionally included in the model for georeferencing and quality control. Adjacent strip connection is enabled by an overlap analysis to further improve connectivity of local networks. A novel angular parametrization based on spherical rotation coordinate system is presented to address the gimbal lock singularity of BBA. Our results suggest that the proposed scheme is a precise real-time monocular SLAM solution for a UAV.
  • Lahtela, Eero (Helsingin yliopisto, 2021)
    Municipal environmental authorities are required to conduct environmental monitoring. Unmanned aerial vehicles, UAVs, may be helpful in environmental monitoring but their applicability as a tool for municipal environmental monitoring has not been studied. In this thesis it was studied, how municipalities have been utilizing UAVs. Additionally, UAVs applicability for environmental monitoring and inspection work was tested using a litter monitoring experiment as an example. In the first part of the study, a questionnaire was sent to municipal environmental authorities in Finland, to municipalities in Sweden and to those participating in Eurocities WG Waste group (n = 512), covering the used applications, their utilization frequencies and successfulness, reasons for failures and future plans. The results were analyzed using descriptive statistics. In the second part of the study, a UAV was utilized in a litter monitoring experiment on four sites in Helsinki. Litter by category and leaves were counted based on visual observations from UAV imagery. The accuracy of UAV imagery detection was assessed by comparing its and ground assessment (GA) results. On one site, a control group also carried out UAV imagery detections in order to assess the magnitude of bias or offset occurring when both the GA and the litter detection from UAV imagery are conducted by a single individual. The Wilcoxon signed rank and Cronbach’s α reliability tests were used for statistical analysis of the results. Response rate of the questionnaire was low, 3.7% (n = 19). The pool of used applications was extensive and covered a variety of monitoring and inspecting targets with emphasis on the presumably manually piloted applications. Utilization was very successful. The most important reasons for failures were poor weather followed by lack of information and expertise. UAVs were included in the future plans of most participants for municipal environmental monitoring purposes. The UAV imagery detection accuracies of litter and leaves compared to the GA results were high, 90.5% for litter and 87.5% for litter and leaves, and no statistically significant differences existed between the assessment results. Especially leaves proved challenging to detect from UAV imagery. The control group’s detection accuracies were 67.9% without and 49.0% with leaves, and with leaves the results differed with statistical significance (p = 0.028). The internal reliability of the control group was relatively high, α = 0.776 without and α = 0.805 with leaves. UAVs are deemed sufficiently accurate and versatile as monitoring and inspecting tools for municipal environmental authorities. They have the capability to complement ground assessments or, with certain prerequisites, even function as an independent monitoring method. Further application and detection method development and research on municipal UAV utilization are needed.
  • Imangholiloo, Mohammad; Saarinen, Ninni; Markelin, Lauri; Rosnell, Tomi; Nasi, Roope; Hakala, Teemu; Honkavaara, Eija; Holopainen, Markus; Hyyppa, Juha; Vastaranta, Mikko (2019)
    Seedling stands are mainly inventoried through field measurements, which are typically laborious, expensive and time-consuming due to high tree density and small tree size. In addition, operationally used sparse density airborne laser scanning (ALS) and aerial imagery data are not sufficiently accurate for inventorying seedling stands. The use of unmanned aerial vehicles (UAVs) for forestry applications is currently in high attention and in the midst of quick development and this technology could be used to make seedling stand management more efficient. This study was designed to investigate the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions. The focus was in retrieving tree density and the height in young seedling stands in the southern boreal forests of Finland. After creating the canopy height model from photogrammetric point clouds using national digital terrain model based on ALS, the watershed segmentation method was applied to delineate the tree canopy boundary at individual tree level. The segments were then used to extract tree heights and spectral information. Optimal bands for calculating vegetation indices were analysed and used for species classification using the random forest method. Tree density and the mean tree height of the total and spruce trees were then estimated at the plot level. The overall tree density was underestimated by 17.5% and 20.2% in leaf-off and leaf-on conditions with the relative root mean square error (relative RMSE) of 33.5% and 26.8%, respectively. Mean tree height was underestimated by 20.8% and 7.4% (relative RMSE of 23.0% and 11.5%, and RMSE of 0.57 m and 0.29 m) in leaf-off and leaf-on conditions, respectively. The leaf-on data outperformed the leaf-off data in the estimations. The results showed that UAV imagery hold potential for reliably characterizing seedling stands and to be used to supplement or replace the laborious field inventory methods.
  • Kuzmin, Anton; Korhonen, Lauri; Kivinen, Sonja; Hurskainen, Pekka; Korpelainen, Pasi; Tanhuanpää, Topi; Maltamo, Matti; Vihervaara, Petteri; Kumpula, Timo (2021)
    European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests.Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras:Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.
  • Hakala, Teemu; Markelin, Lauri; Honkavaara, Eija; Scott, Barry; Theocharous, Theo; Nevalainen, Olli; Näsi, Roope; Suomalainen, Juha; Viljanen, Niko; Greenwell, Claire; Fox, Nigel (MDPI, 2018)
    Sensors
    Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Pérot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK).
  • Suomalainen, Juha; Oliveira, Raquel A.; Hakala, Teemu; Koivumäki, Niko; Markelin, Lauri; Näsi, Roope; Honkavaara, Eija (Elsevier, 2021)
    Remote Sensing of Environment
    Multi- and hyperspectral cameras on drones can be valuable tools in environmental monitoring. A significant shortcoming complicating their usage in quantitative remote sensing applications is insufficient robust radiometric calibration methods. In a direct reflectance transformation method, the drone is equipped with a camera and an irradiance sensor, allowing transformation of image pixel values to reflectance factors without ground reference data. This method requires the sensors to be calibrated with higher accuracy than what is usually required by the empirical line method (ELM), but consequently it offers benefits in robustness, ease of operation, and ability to be used on Beyond-Visual Line of Sight flights. The objective of this study was to develop and assess a drone-based workflow for direct reflectance transformation and implement it on our hyperspectral remote sensing system. A novel atmospheric correction method is also introduced, using two reference panels, but, unlike in the ELM, the correction is not directly affected by changes in the illumination. The sensor system consists of a hyperspectral camera (Rikola HSI, by Senop) and an onboard irradiance spectrometer (FGI AIRS), which were both given thorough radiometric calibrations. In laboratory tests and in a flight experiment, the FGI AIRS tilt-corrected irradiances had accuracy better than 1.9% at solar zenith angles up to 70◦. The system’s lowaltitude reflectance factor accuracy was assessed in a flight experiment using reflectance reference panels, where the normalized root mean square errors (NRMSE) were less than ±2% for the light panels (25% and 50%) and less than ±4% for the dark panels (5% and 10%). In the high-altitude images, taken at 100–150 m altitude, the NRMSEs without atmospheric correction were within 1.4%–8.7% for VIS bands and 2.0%–18.5% for NIR bands. Significant atmospheric effects appeared already at 50 m flight altitude. The proposed atmospheric correction was found to be practical and it decreased the high-altitude NRMSEs to 1.3%–2.6% for VIS bands and to 2.3%– 5.3% for NIR bands. Overall, the workflow was found to be efficient and to provide similar accuracies as the ELM, but providing operational advantages in such challenging scenarios as in forest monitoring, large-scale autonomous mapping tasks, and real-time applications. Tests in varying illumination conditions showed that the reflectance factors of the gravel and vegetation targets varied up to 8% between sunny and cloudy conditions due to reflectance anisotropy effects, while the direct reflectance workflow had better accuracy. This suggests that the varying illumination conditions have to be further accounted for in drone-based in quantitative remote sensing applications.
  • Riihimäki, Henri; Luoto, Miska; Heiskanen, Janne (2019)
    Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m x 250 m sites in Fennoscandian tundra with varying productivity ana FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models. First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data. The overall classification accuracies for the UAS sites were >= 90%. The UAS-FCover were strongly related to the tested VIs (D-2 89% at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research. Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents.
  • Maseyk, Kadmiel; Atherton, Jon; Thomas, Rick; Wood, Kieran; Tausz-Posch, Sabine; MacArthur, Alasdair; Porcar-Castell, Albert; Tausz, Michael (IEEE, 2018)
    IEEE International Symposium on Geoscience and Remote Sensing IGARSS
    The response of ecosystems to increasing atmospheric CO2 will have significant, but still uncertain, impacts on the global carbon and water cycles. A lot of infounation has been gained from Free Air CO2 Enrichment (FACE) experiments, but the response of mature forest ecosystems remains a significant knowledge gap. One of the challenges in FACE studies is obtaining an integrated measure of canopy photosynthesis at the scale of the treatment ring. A new remote sensing approach for measuring photosynthetic activity is based on Solar Induced Fluorescence (SIF), which is emitted by plants during photosynthesis, and is closely linked to the rates and regulation of photosynthesis. We proposed that UAV-based SIF measurements, that enable the spectrometer field of view to be targeted to the treatment ring, provide a unique opportunity for investigating the dynamics of photosynthetic responses to elevated CO2. We have successfully tested this approach in a new FACE site, located in a mature oak forest in the UK. We flew a series of flights across the experiment arrays, collecting a number of spectra. We combined these with ground-based physiological and optical measurements, and see great promise in the use of UAV-based SIF measurements in FACE and other global change experiments.
  • Änäkkälä, Mikael (Helsingin yliopisto, 2020)
    The number of drones has increased in both the private and corporate sectors. There is also an interest in the use of drones in agriculture since by using them the large fields can be monitored easily. Automatic flight systems of drones are simple to use. More accurate overview of the field can be got by utilizing the drones than by making observations from the side of the field. With aerial photographs the measures for the field can be planned further. For example, based on the photos pesticide spraying or fertilize spreading can be planned for the field. Drones can also be used to estimate crop biomasses. With drones the development of the crops is possible to observe as a timeseries during the growing season. The aim of this study was to explore the use of multispectral images and 3D models in crop monitoring. Crop leaf area index (LAI), biomass and chlorophyll content were measured. There were 8 different plants/fertilization levels in this study. In this study, a multispectral camera and a RGB-camera were used to estimate crops features. With a multispectral camera the reflectance values of the vegetation, which described how much of the incoming sun radiation was reflected back from the vegetation, were able to determine. The multispectral camera had five spectral bands (blue, green, red, red edge and NIR). Based on these bands NDVI vegetation index was calculated. The reflectance values and vegetation indices were compared to the dry matter mass, LAI, and chlorophyll content determinations of the vegetation. From the images of the RGB-camera 3D-models were created to calculate crop volumes. Calculated volumes were compared to crop dry matter mass and LAI measurements. Linear regression analysis was used to examine the relationship between the variables calculated from the images and the parameters determined from the crops on the field. According to these results, the variables determined from the multispectral images explained the dry matter mass and leaf area index of the crop slightly less than the 3D-models determined from the RGB images. The strongest determined dependence of the data recorded by the multispectral camera was between the faba bean LAI and NDVI (R2 = 0,85). The relationship between the reflection/index data of multispectral camera and crop parameter was weak: average coefficient of determination for dry matter mass of the crop was 0.15, for chlorophyll content 0.14, and for LAI 0,21. The highest coefficient of determination for 3D model of crop volume was between the dry matter mass of oats (R2 = 0.91). The mean coefficient of dependence was 0.69 for the relationship between the plant dry matter masses and 3D model volumes. The mean coefficient of determination for the relationship between the leaf area index of plants and the 3D model volumes was 0.57. Based on these results, from the multispectral camera data, the NDVI index was best suited to determine the crops dry matter mass, leaf area index, and chlorophyll content. However, there were differences in the dependencies between different spectral bands/NDVI index and plant properties determined from different crops. 3D models produced stronger dependences for estimating crop dry matter mass and leaf area index than the quantities determined from multispectral images. Analyzing the data with more sophisticated calculation methods utilizing the values of several spectral bands and the indices in the same time would probably have been a more efficient method to analyzing the data than the current used linear regression used in this study. Removing errors, caused by external factors, from multispectral images was found to be very difficult. Especially reflectance values of dry soil differed clearly from vegetations values. Further studies are needed to develop vegetation indices that can reduce errors caused by external factors. In addition, data processing of images should be developed to utilize multiple spectral bands and vegetation indices to determine the relationship between crop characteristics and variables measured from images. In addition, different plant species imaging techniques should be investigated, as different plants have different reflection values.
  • Rissanen, Kaisa; Martin-Guay, Marc-Olivier; Riopel-Bouvier, Anne-Sophie; Paquette, Alain (2019)
    Biodiversity affects ecosystem functioning in forests by, for example, enhancing growth and altering the forest structure towards greater complexity with cascading effects on other processes and trophic levels. Complexity in forest canopy could enhance light interception and form a link between diversity and productivity in polyculture forests, but the effect of canopy structure on light interception is rarely directly measured. We modelled the canopy surface structure of a tree diversity experiment by photographing it using unmanned aerial vehicle (UAV) and combining the photos into a digital elevation model with photogrammetry tools. We analysed the effects of tree diversity and functional diversity on canopy structural complexity and light interception with a structural equation model. Our results show that: a) increased structural complexity of the canopy reduces light interception, whereas b) tree diversity increases the structural complexity of the canopy, and has a dual impact on light interception. Tree diversity decreased light interception through the structural complexity of the canopy but increased it probably through canopy packing and crown complementarity. However, the effects of both tree diversity and structural complexity of canopy were smaller than the effect of the functional identities of the tree species, especially the differences between deciduous and evergreen trees. We conclude that more complexity in canopy structure can be gained through increased tree diversity, but complex canopy structure does not increase light interception in young forests.
  • Jaakkola, A (Aalto University, 2015)
    Aalto University publication series DOCTORAL DISSERTATIONS
  • Yrttimaa, Tuomas; Saarinen, Ninni; Kankare, Ville; Viljanen, Niko; Hynynen, Jari; Huuskonen, Saija; Holopainen, Markus; Hyyppa, Juha; Honkavaara, Eija; Vastaranta, Mikko (2020)
    Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (H-g) and mean stem volume (V-mean). Most notably, the root-mean-square-error (RMSE) in H-g improved from 0.8 to 0.58 m and the bias improved from -0.75 to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, V-mean, H-g, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.
  • Nurmilaukas, Olli (Helsingin yliopisto, 2020)
    The condition of Tahmelanlähde spring in city of Tampere has been under discussion for over two decades. Between 1906–1966, the spring was being used for municipal water supply and the water quality was good. The quality of discharging groundwater has since heavily deteriorated, bearing now high concentrations of iron, manganese, nitrogen, phosphorus and very low oxygen. The cause of this deterioration has remained unclear. The aims of this study were to increase the hydrogeological knowledge of Tahmela-Pispala area in order to get a better understanding of the regional groundwater flow patterns and sources of the groundwater discharging at the artesian spring area, to assess the cause for the spring deterioration and to give suggestions to a possible rehabilitation plan. Tahmelanlähde spring is located on a clay or silt soil under artesian circumstances, down the southern slope of Pispalanharju interlobate esker formation. The esker forms a longitudinal neck between Lake Näsijärvi and Lake Pyhäjärvi, rising up to 160 meters above sea level. The water level of Lake Näsijärvi is approx. 95 m a.s.l. and the water level of Lake Pyhäjärvi approx. 77 m a.s.l. Considering the distance of only a few hundred meters between these two lakes, the difference of 18 meters in the lake water levels is quite unusual in Finland’s geological context, especially because the lakes are separated by a major esker formation. For the assessment of the hydrogeological features in the study area we had two field campaigns including ground penetrating radar (GPR) survey, thermal infrared survey using unmanned aerial vehicle (UAV-TIR), measuring of water tables as well as water sampling from springs, surface water bodies, groundwater observation wells and groundwater discharging into the Lake Pyhäjärvi. 23 water samples were analyzed for main ion composition, stable isotopic (δ18O / δD) composition, pH, EC and trace elements such as iron and manganese. 14 samples were additionally analyzed for CODMn, N, P, O and microbial indicators. Some previous studies have suggested infiltration of Lake Näsijärvi water into the esker. Our results reveal that most of the groundwater in the Pispalanharju area contain a variable amount of surface water component. The samples east from the spring present good-quality groundwater and show nonexistent surface water impact. This and the complex sedimentology revealed by the GPR survey indicate that the regional groundwater flow patterns are not simple and there are at least two water components with different origins discharging at Tahmelanlähde spring. The results imply that the primary cause for the spring deterioration could be a major shift in the groundwater – surface water interaction in the northern esker area, probably driven by urbanization and the heavy construction during the last few decades. The study was a collaboration between the City of Tampere, Pirkanmaa Center for Economic Development, Transport and Environment (ELY Center) and University of Helsinki, Department of Geosciences and Geography.
  • Roiha, Johanna; Heinaro, Vili Einari; Holopainen, Markus (2021)
    Conducting archaeological site surveys is time consuming, and large sites may have many small features or structures that are difficult to locate and interpret. Vegetation cover and dense forest hide small structures, like cairns, while at the same time forest cover can cause problems for LiDAR tools. In this case study, drone-based ALS (airborne laser scanning) was tested as an archaeological site survey tool. The research site was complex and located partially in a forested area, which made it possible to evaluate how forest cover affects data. The survey methods used were rather simple: visual analysis, point density calculations in the forest area, and, for site interpretation purposes, digitizing observations and viewshed analysis. Using straightforward methods allowed us to evaluate the minimum time and skills needed for this type of survey. Drone-based ALS provided good results and increased knowledge of the site and its structures. Estimates of the number of cairns interpreted as graves more than doubled as a result of the high-accuracy ALS data. Based on the results of this study, drone-based ALS could be a suitable high-accuracy survey method for large archaeological sites. However, forest cover affects the accuracy, and more research is needed.
  • Hyyppa, Eric; Hyyppa, Juha; Hakala, Teemu; Kukko, Antero; Wulder, Michael A.; White, Joanne C.; Pyorala, Jiri; Yu, Xiaowei; Wang, Yunsheng; Virtanen, Juho-Pekka; Pohjavirta, Onni; Liang, Xinlian; Holopainen, Markus; Kaartinen, Harri (2020)
    Surveying and robotic technologies are converging, offering great potential for robotic-assisted data collection and support for labour intensive surveying activities. From a forest monitoring perspective, there are several technological and operational aspects to address concerning under-canopy flying unmanned airborne vehicles (UAV). To demonstrate this emerging technology, we investigated tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV. To this end, we mounted a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV. Using the under-canopy flying UAV, we collected SLAM-corrected point cloud data in a boreal forest on two 32 m x 32 m test sites that were characterized as sparse (n = 42 trees) and obstructed (n = 43 trees), respectively. Novel data processing algorithms were applied for the point clouds in order to detect the stems of individual trees and to extract their stem curves and diameters at breast height (DBH). The estimated tree attributes were compared against highly accurate field reference data that was acquired semi-manually with a multi-scan terrestrial laser scanner (TLS). The proposed method succeeded in detecting 93% of the stems in the sparse plot and 84% of the stems in the obstructed plot. In the sparse plot, the DBH and stem curve estimates had a root-mean-squared error (RMSE) of 0.60 cm (2.2%) and 1.2 cm (5.0%), respectively, whereas the corresponding values for the obstructed plot were 0.92 cm (3.1%) and 1.4 cm (5.2%). By combining the stem curves extracted from the under-canopy UAV laser scanning data with tree heights derived from above-canopy UAV laser scanning data, we computed stem volumes for the detected trees with a relative RMSE of 10.1% in both plots. Thus, the combination of under-canopy and above-canopy UAV laser scanning allowed us to extract the stem volumes with an accuracy comparable to the past best studies based on TLS in boreal forest conditions. Since the stems of several spruces located on the test sites suffered from severe occlusion and could not be detected with the stem-based method, we developed a separate work flow capable of detecting trees with occluded stems. The proposed work flow enabled us to detect 98% of trees in the sparse plot and 93% of the trees in the obstructed plot with a 100% correction level in both plots. A key benefit provided by the under-canopy UAV laser scanner is the short period of time required for data collection, currently demonstrated to be much faster than the time required for field measurements and TLS. The quality of the measurements acquired with the under-canopy flying UAV combined with the demonstrated efficiency indicates operational potential for supporting fast and accurate forest resource inventories.
  • Näsi, Roope; Honkavaara, Eija; Lyytikäinen-Saarenmaa, Päivi Marja Emilia; Blomqvist, Minna; Litkey, Paula; Hakala, Teemu; Viljanen, Niko; Kantola, Tuula Anneli; Tanhuanpää, Topi-Mikko Tapio; Holopainen, Markus Edvard (2015)
    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.