Estimation of tree height using UAV photogrammetric data

Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS point cloud metrics. Field measurements of tree height and effective leaf area index (LAIe) were acquired from a total of 200 and 26 trees, respectively. Comparable accuracies were obtained in the tree height and LAI estimations using ALS and DAI data independently. Tree height estimated from DAI-based metrics (Percentile 90 (P90) and minimum height (MinH)) yielded a coefficient of determination (R2) = 0.71 and a root mean square error (RMSE) = 0.71 m while models derived from ALS-based metrics (P90) yielded an R2 = 0.80 and an RMSE = 0.55 m. The estimation of LAI from DAI-based NDVI using Percentile 99 (P99) yielded an R2 = 0.62 and an RMSE = 0.17 m2/m−2. A comparative analysis of LAI estimation using ALS-based metrics (laser penetration index (LPI), interquartile distance (IQ), and Percentile 30 (P30)) yielded an R2 = 0.75 and an RMSE = 0.14 m2/m−2. The results provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories.

The forest inventory technique by applying remote sensing technology has become a new breakthrough in technological developments in forest inventory activities. Unmanned Aerial Vehicle (UAV) imagery with camera sensor is one of the inventory tools that produce data with high spatial resolution. The level of spatial resolution of the image is strongly influenced by the flying height of the UAV for a certain camera’s focus. In addition, flight height also affects the acquisition time and accuracy of inventory results, although there is still little research on this matter. The study aims to (a)evaluate the effect of various flying heights on the accuracy of tree height measurements through UAV imagery for every stand age class, (b).estimate the trees diameter and canopy cover for every stand age class. Stand height was estimated using Digital Surface Models (DSM), Digital Terrain Models (DTM) and Orthophoto. DSM and DTM were built by converting orthophoto to pointclouds using the PIX4Dmapper based on Structure From Motion (SFM) on the photogrammetric method to reconstruct topography automatically. Meanwhile, the tree cover canopy was estimated using the All Return Canopy Index (ARCI) formula. The results show that the flight height of 100 meters produces a stronger correlation than the flying height of 80 meters and 120 meters in estimating tree height, based on the high coefficient of determination (R2) and the low root mean square error (RMSE) value. In addition, tree canopy estimation analysis using ARCI has a maximum difference of 9.8% with orthophoto visual delineation. Key words: canopy height model (CHM), digital surface models (DSM), digital terrain models (DTM), forest inventory, UAV image

With the LiangShui forestry centre as study area, high-density LiDAR data and to synchronously processed high-resolution digital image are taken as data source to extract Individual Tree Height. The LiDAR data of the study area is filtered and classified, using TIN Filter to extract the ground echo points and trees echo points. Then these ground echo points generate Digital Elevation Model (DEM), and these trees echo points generate Digital Surface Model (DSM). Then the DEM and DSM are Taken as a subtraction to obtain Canopy Height Model (CHM), then the object-oriented approach is used to segment air digital image. Through multi-scale and canopy-model which create image objects and class division level, with the nearest neighbor distance and member function, the image objects are classified, and re-segmentation is based on classification results. And the edge is optimized to accurately identify individual tree. The canopy polygon obtained after image segmentation and CHM were superimposed to calculate polygon maximum elevation difference from LiDAR data as a tree height. Associated with the measured height analysis, the accuracy is 92.04%.

Benefiting from current unmanned air vehicle (UAV) and remote sensing techniques, the present study aims to estimate tree count (TC), tree height (TH), and tree crown cover area (TCCA) in a young Calabrian pine stand via canopy height model (CHM). Overlay images obtained using Quadcopter were used to generate two spatial three-dimensional (3D) cloud points in two different qualities. Point clouds were processed using R program in order to produce tree data using CHM. The sensitivity of CHM-based tree data was revealed using 318 tree measurements in 32 different sampling units. Estimation and measurement values were classified based on their structure from motion (SfM) quality and cover classes, and the statistical relationships among them were analyzed. Without any classification, R2 was calculated for TC, THMean, and TCCATotal estimations and field measurements. R2 values were calculated as 0.865, 0.778, and 0.869, respectively, for SfMHighest CHM, while they were calculated as 0.863, 0.736, and 0.843, respectively, for SfMMedium CHM. In addition, sensitivity and performance ranking in different groups were determined based on root mean square error (RMSE) and mean absolute percentage error (MAPE) values. A significant difference was observed among groups in terms of quality and cover for TH, while no significant differences were observed for TCCA. Therefore, it is possible to estimate the properties of SfM CHM-based young coniferous stand. It was understood that tree density, crown shape, and branching influenced the accuracy of the present study. The developed UAV (Drone)-SfM is a promising technique for further small-scale forestry studies.

BackgroundDetermining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research. The geometric-optical mutual shadowing (GOMS) model can be used to invert the forest canopy structural parameters at the regional scale. However, this method can obtain only the ratios among the horizontal canopy diameter (CD), tree height, clear height, and vertical CD. In this paper, we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale. We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.ResultsThe semi-variance model can be used to calculate the CD at the regional scale that closely matches (mainly with in a range from − 1 to 1 m) the CD derived from the canopy height model (CHM) data. The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small, with a root mean square error (RMSE) of 1.96 for a 500-m area with high fractional vegetation cover (FVC) (i.e., forest area coverage index values greater than 0.8). Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height. And the error caused by the unmatched spatial resolution is small in dense forest.ConclusionsThe semi-variance model can be used to calculate the CD at the regional scale, together with the canopy structure parameters inverted by the GOMS model, the mean tree height at the regional scale can be obtained. Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.

To address the current difficult problem of scientifically assessing the health status of date palm trees due to a single parameter for date palm health assessment, an imperfect index system, and low precision. In this paper, using jujube trees in 224 regiment of the 14th division of Xinjiang Production and Construction Corps “Kunyu city” as the research object, we carried out the inversion study of various physicochemical parameters of jujube trees (canopy chlorophyll content, leaf area index (LAI), tree height, canopy area) using the unmanned aerial vehicle (UAV) hyperspectral imagery of jujube trees during the period of fruit expansion, and put forward a model for assessing the health of jujube trees based on multiple physicochemical parameters. First, we calculated six spectral indices for inversion of chlorophyll content and four spectral index for inversion of LAI, analyzed the spectral index with high correlation with chlorophyll content and LAI of jujube trees canopy, and constructed the inversion models of chlorophyll content and LAI. Second, the Mask R-CNN model was used to achieve jujube trees’ canopy segmentation and area extraction, and the segmented canopy was matched with the Canopy Height Model (CHM) for jujube trees’ height extraction. Finally, based on the four physicochemical parameters of inversion, we construct four jujube trees’ health assessment models, namely, Partial Least Squares Regression Analysis (PLSR), Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT). The results showed that the R2 of the PLSR tree health assessment model constructed based on the multi-physical and chemical parameters of chlorophyll content, LAI, tree height, and canopy area was 0.853, and the RMSE was 0.3. Compared with the jujube trees’ health assessment models constructed by RF, SVM, and DT, the R2 increased by 0.127, 0.386, and 0.165, and the RMSE decreased by 0.04, 0.175, and 0.063, respectively. This paper can achieve rapid and accurate inversion of multi-physical and chemical parameters of jujube trees with the help of UAV hyperspectral images, and the PLSR model constructed based on multi-physical and chemical parameters can accurately assess the health status of jujube trees and provide a reference for a scientific and reasonable assessment of jujube trees’ health.

ABSTRACTIt was demonstrated in the past that radar data is useful to estimate aboveground biomass due to their interferometric capability. Therefore, the potential of a globally available TanDEM-X digital elevation model (DEM) was investigated for aboveground biomass estimation via canopy height models (CHMs) in a tropical peat swamp forest. However, CHMs based on X-band interferometers usually require external terrain models. High accurate terrain models are not available on global scale. Therefore, an approach exclusively based on TanDEM-X and the decrease of accuracy compared to an approach utilizing a high accurate terrain model is assessed. In addition, the potential of X-band interferometric heights in tropical forests needs to be evaluated. Therefore, two CHMs were derived from an intermediate TanDEM-X DEM (iDEM; as a precursor for WorldDEMTM) alone and in combination with lidar measurements used as terrain model. The analysis showed high accuracies (root mean square error [RMSE] = 5 m) for CHMs based on iDEM and reliable estimation of aboveground biomass. The iDEM CHM, exclusively based on TanDEM-X, achieved a poor R2 of 0.2, nonetheless resulted in a cross-validated RMSE of 54 t ha−1 (16%). The low R2 suggested that the X-band height alone was not sufficient to estimate an accurate CHM, and thus the need for external terrain models was confirmed. A CHM retrieved from the difference of iDEM and an accurate lidar terrain model achieved a considerably higher correlation with aboveground biomass (R2 = 0.68) and low cross-validated RMSE of 24.5 t ha−1 (7.5%). This was higher or comparable to other aboveground biomass estimations in tropical peat swamp forests. The potential of X-band interferometric heights for CHM and biomass estimation was thus confirmed in tropical forest in addition to existing knowledge in boreal forests.

AimsUncrewed aerial vehicles (UAV), or drones, have become more affordable and easier to use, resulting in increased UAV applications in ecology and conservation. However, solar illumination, vegetation phenology and prevailing weather conditions will impact the quality of the derived products to differing degrees. In this study, we investigate how seasonal differences in solar illumination, tree foliage and weather conditions impact the accuracy of digital elevation models (DEM) and canopy height models (CHM) in a heterogeneous boreal landscape.MethodsWe compared DEMs and CHMs derived from drone photogrammetry with DEMs and CHMs produced from a drone‐mounted laser scanner across three seasons with different solar illumination, tree foliage and weather conditions during leaf‐off and leaf‐on seasons. Photogrammetric height models were evaluated across three land‐cover classes consisting of open areas, sparse‐forest and forest. The most accurate CHM for sparse‐forest was produced during summer under overcast conditions, whereas for the forest class, summer under clear skies was best.ResultsStructure from motion (SfM) photogrammetry performed well against the LiDAR survey in most cases with correlations between sampled points of up to R2 = 0.995. Root mean square errors (RMSEs) were <1.5 m in all DEMs and as low as 0.31 m in autumn clear‐sky data over open terrain. CHM RMSEs were somewhat higher in all cases except under winter overcast conditions when the RMSE for sparse‐forest reached 6.03 m.ConclusionsWe have shown that SfM photogrammetry is surprisingly robust to variations in vegetation type, tree phenology and weather, and performs well in comparison with a reference LiDAR data set. Our results show that, in boreal forests, autumn is the preferred season under clear‐sky conditions for DEM generation from SfM photogrammetry across all land‐cover classes, whereas summer is preferred for CHM modelling with a small trade‐off between overcast and clear‐sky conditions over different vegetation types. These results can help potential SfM users in ecology and forestry plan missions and review the quality of products derived from drone photogrammetry products.

To accurately assess landslide susceptibility and model debris flow paths, acquisition of high-resolution elevation data is essential. These data enable precise topographical analysis, considering factors like slope and curvature. But most of the time such high-resolution data will have inaccuracies due to vegetation, especially in tropical region. This study proposes a comprehensive approach that integrates field data and Structure from Motion (SfM) technology to rectify such DEM inaccuracies caused by dense vegetation. The goal is to enhance the accuracy of landslide simulations and volumetric analysis for effective post-disaster management. The Western Ghats, running parallel to India's western coast, has experienced a surge in rainfall-induced landslides, resulting in significant casualties in recent years. Notably, the Pettimudi village landslide in 2020 caused 70 deaths. The unique geomorphological features of the Western Ghats, such as concave curvature, colluvium deposits, and evidence of paleo landslides, contribute to the area's susceptibility. The study emphasizes the need for detailed assessment and mitigation strategies. The proposed method aims to improve the post-event high-resolution DEM accuracy by integrating field-collected elevation values and utilizing Agisoft Metashape software with the SfM algorithm. The rectification process involves combining elevation differences measured during a field study with photogrammetric elevation data. The field-collected elevation differences are crucial for rectifying these points, and enhancing the Digital Elevation Model (DEM) accuracy. The inaccessible source region is improved using a SfM-created DEM from drone footage, resulting in a more accurate post-event DEM. Correcting the 1 m DEM using an SfM-generated DEM proves challenging but significantly improves detail and accuracy, especially over the landslide source area. The impact of the rectification on accuracy is assessed by comparing volumes calculated from the initial DEM and the newly corrected DEM. The difference in volumes of debris depletion and accumulation, computed using initial and corrected DEMs, highlights variations, with depleted volumes being significantly larger due to the extensive depth increase over the landslide source region. These volumes are then utilized in Rapid Mass Movement Simulation (RAMMS) to validate the rectification process and enhance landslide impact predictions. Debris flow simulations in RAMMS, utilizing the rectified 1 m DEM, show specific outcomes at the landslide source region and toe of the landslide. The study emphasizes the importance of integrating SfM technology with field data to improve DEM accuracy, acknowledging the significance of additional field data for further refinement. The potential adoption of highly precise aerial imagery from Unmanned Aerial Vehicle (UAV) surveys is suggested to further enhance the SfM DEM. Debris flow modeling with RAMMS serves as a vital step in validating the accuracy and reliability of the rectified elevation model. Keywords: Landslides, Western Ghats, Pettimudi, Digital elevation model (DEM), Structure-from-Motion (SfM) technology, Rapid Mass Movement Simulation (RAMMS)

Canopy height change (CHC) is one of the key characteristics of forest dynamics, associated with the fluctuations in forest above-ground biomass and carbon stocks. Field measurements and Airborne Laser Scanning (ALS) point clouds can be used to detect CHC; however, they have limited availability in space and time, making it challenging to map CHC over large areas. Alternatively, very high-resolution (VHR) satellite stereo imagery plays an increasingly vital role in estimating fine-scaled digital surface models (DSMs) across landscapes. However, its capability and potential to estimate canopy height model (CHM) and CHC has not been widely explored. Using ALS-derived CHM in 2011 and 2015 and the four-year CHC as references, we evaluated stereo-based CHM and CHC from WorldView-2 over five woody parks in Columbus, Ohio, USA. We also integrated stereo-based CHM with vegetation indices from Landsat 7 to improve CHM and CHC estimation with machine learning methods. Our results showed that VHR stereo imagery captured similar spatial patterns of CHM with ALS data but significantly overestimated CHM. Moreover, the ALS-derived CHC ranged from 1.6±1.9 m (mean ± standard deviation) to 3.1±1.2 m, as compared to from -1.3±0.7 m to 1.1±1.7 m for stereo-based CHC, indicating the limitation of CHC estimation by stereo imagery alone. Among six widely used machine learning methods, Gradient Boosting Regression method provided the most reliable estimates of CHM, with a correlation coefficient R of 0.64 and a root-mean-square error (RMSE) of 3.1 m (11.1%). Stereo-based CHM and vegetation indices explained more than 70% of CHM variability, substantially improving the estimation of 4-year CHC. Our results suggested that VHR stereo imagery alone has limitations in estimating CHM and CHC. The combination of remote-sensing structural (stereo-based CHM) and spectral (vegetation indices) information improves the CHM and CHC estimations.

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This study introduces a new product named the Hyper Point Cloud (HPC), derived from FW LiDAR data, and explores its potential applications, such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which shows promise as a solution to the constraints of using FW LiDAR data. The results of the HPC present a new direction for handling FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has higher maximum intensity (MAXI) and mean intensity (MI) than the vegetation region, while having lower total intensity (TI) and number of intensities (NI) at a given grid cell. Our analysis of intensity distribution contours at the individual tree level exhibit similar patterns, indicating that the MAXI and MI decrease from the tree crown center to the tree boundary, while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR-derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives for the use of FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

Accurate and precise topographic information from Digital Elevation Models (DEM) is of fundamental requirement for many geoscience and engineering applications. With a goal of providing accurate global topographic products, German Aerospace Centre (DLR) and Airbus Defence and Space developed X-band TanDEM-X mission. The TanDEM-X Global DEM is generated with a pixel spacing of 12×12 m from interferometric data acquired in strip map mode with a resolution of 3.3 m [1]. In this study, the vertical accuracy of TanDEM-X Global DEM is assessed by comparing it with various elevation datasets. The elevation datasets include DGPS measurements, SRTM 30m, and 90 m DEM, CartoSat DEM, LiDAR data, photogrammetry data, intermediate and fused DEMs’ derived from TanDEM-X CoSSC [2], ICESat elevations and the newly released TanDEM-X 90 m DEM. The study areas include Bihar (flat terrain), Mumbai (moderate and urban terrain), Godavari (moderate and hilly), Koyna (hilly and vegetated), Manali (rugged Himalayan) and Kartarniaghat Wild Life Sanctuary-KWLS (flat and forest) with different terrain conditions. Widely used statistical parameters such as mean, standard deviation (SD) and root mean square error (RMSE) are used for vertical accuracy assessment [3]. The minimum and maximum differences between elevations are also noted, which are useful in exploiting quality of DEM under evaluation. The accuracy of the TanDEM-X Global DEM is found to be less than 1.6 m, 4 m, 5 m, 13 m, 6 m for plain, moderate relief, hilly, Himalayan, vegetated terrains respectively. In vegetated areas (KWLS), we observed an 8 m difference in tree height estimation using TanDEM-X Global DEM and GPS measurement. Elevation and difference profiles were drawn to understand the vertical accuracy of these DEMs’. In all cases, the elevations of TanDEM-X Global DEM closely matched with heights derived from DGPS, ICESAT, LiDAR and photogrammetry data.

Automated feature extraction based on prototypes is only partially successful when applied to remotely sensed imagery of natural scenes due to the complexity and unpredictability of the shape and geometry of natural features. Here, a new method is developed for extracting the locations of treetops by applying GIS (Geographical Information System) overlay techniques and morphological functions to high spatial resolution airborne imagery. This method is based on the geometrical and spatial properties of tree crowns. Airborne data of the study site in the New Forest, UK included colour aerial photographs, LIDAR (Light Detection And Ranging) and ATM (Airborne Thematic Mapper) imagery. A DEM (Digital Elevation Model) was generated from LIDAR data and then subtracted from the original LIDAR image to create a Canopy Height Model (CHM). A set of procedures using image contouring and the manipulation of the resulting polygons was implemented to extract treetops from the aerial photographs and the CHM. Criteria were developed and threshold values were set using a supervised approach for the acceptance or rejection of features based on field knowledge. Tree species were mapped by classifying the ATM data and these data were co‐registered with the treetop layer. For broadleaved deciduous plantations the success of treetop extraction using aerial photographs was 91%, but was much lower using LIDAR data. For semi‐natural forests, the LIDAR produced better results than the aerial photographs with a success of 80%, which was considered high, given the complexity of these uneven aged stands. The methodology presented here is easy to apply as it is implemented within a GIS and the final product is an accurate map with information about the location, height and species of each tree.

Nowadays, due to the rapid development of lightweight unmanned aerial vehicles (UAV), remote sensing systems of ultra-high resolution have become available to many researchers. Conventional ground-based measurements for assessing tree stand attributes can be expensive, as well as time- and labor-consuming. Here, we assess whether remote sensing measurements with lightweight UAV can be more effective in comparison to ground survey methods in the case of temperate mixed forests. The study was carried out at the Prioksko-Terrasny Biosphere Nature Reserve (Moscow region, Russia). This area belongs to a coniferous-broad-leaved forest zone. Our field works were carried out on the permanent sampling plot of 1 ha (100×100 m) established in 2016. The coordinates of the plot center are N 54.88876°, E 37.56273° in the WGS 84 datum. All trees with DBH (diameter at breast height) of at least 6 cm (779 trees) were mapped and measured during the ground survey in 2016 (See Fig. 1 and Table 1). Mapping was performed with Laser Technology TruPulse 360B angle and a distance meter. First, polar coordinates of each tree trunk were measured, and then, after conversion to the cartesian coordinates, the scheme of the stand was validated onsite. Species and DBH were determined for each tree. For each living tree, we detected a social status class (according to Kraft). Also for living trees, we measured the tree height and the radii of the crown horizontal projection in four cardinal directions. A lightweight UAV Phantom 4 (DJI-Innovations, Shenzhen, China) equipped with an integrated camera of 12Mp sensor was used for aerial photography in this study. Technical parameters of the camera are available in Table 2. The aerial photography was conducted on October 12, 2017, from an altitude of 68 m. The commonly used mosaic flight mode was used with 90% overlapping both for side and front directions. We applied Agisoft Metashape software for orthophoto mosaic image and dense point cloud building. The canopy height model (CHM) was generated with lidR package in R. We used lasground() function and cloth simulation filter for classification of ground points. To create a normalized dataset with the ground at 0, we used spatial interpolation algorithm tin based on a Delaunay triangulation, which performs a linear interpolation within each triangle, implemented in the lasnormilise() function. CHM was generated according to the pit-free algorithm based on the computation of a set of classical triangulations at different heights. The location and height of individual trees were automatically detected by the function FindTreesCHM() from the package rLIDAR in R. The algorithm implemented in this function is local maximum with fixed window size. Accuracy assessment of automatically detected trees (in QGIS software) was performed through visual interpretation of orthophoto mosaic and comparison with ground survey data. The number of correctly detected trees, omitted by the algorithm and not existing but detected trees were counted. As a result of aerial photography, 501 images were obtained. During these data processing with the Metashape, dense point cloud of 163.7 points / m2 was generated. CHM with 0.5 m resolution was calculated. According to the individual-tree detection algorithm, 241 trees were found automatically (See Fig. 2A). The total accuracy of individual tree detection was 73.9%. Coniferous trees (Pinus sylvestris and Picea abies) were successfully detected (86.0% and 100%, respectively), while results for birch (Betula spp.) required additional treatment. The algorithm correctly detected only 58.2% of birch trees due to false-positive trees (See Fig. 2B and Table 3). These results confirm the published literature data obtained for managed tree stands. Tree heights retrieved from the UAV were well-matched to ground-based method results. The mean tree heights retrieved from the UAV and ground surveys were 25.0±4.8 m (min 8.2 m, max 32.9 m) and 25.3±5.2 m (min 5.9 m, max 34.0 m), respectively (no significant difference, p-value=0.049). Linear regression confirmed a strong relationship between the estimated and measured heights (y=k*x, R2 =0.99, k=0.98) (See Fig. 3A). Slightly larger differences in heights estimated by the two methods were found for birch and pine; for spruce, the differences were smaller (See Fig. 3B and Table 4). We believe that ground measurements of birch and pine height are less accurate than for spruce due to different crown shapes of these trees. So, our results suggested that UAV data can be used for tree stand attributes estimation, but automatically obtained data require validation.

Integrating low-altitude drone based-imagery and OBIA for mapping and manage semi natural grassland habitats