Peter Surový

Determining tree height and crown diameter from high-resolution UAV imagery

ABSTRACT Advances in computer vision and the parallel development of unmanned aerial vehicles (UAVs) allow for the extensive use of UAV in forest inventory and in indirect measurements of tree features. We used UAV-sensed high-resolution imagery through photogrammetry and Structure from Motion (SfM) to estimate tree heights and crown diameters. We reconstructed 3D structures from 2D image sequences for two study areas (25 × 25 m). Species composition for Plot 1 included Norway spruce (Picea abies L.) together with European larch (Larix decidua Mill.) and Scots pine (Pinus sylvestris L.), whereas Plot 2 was mainly Norway spruce and Scots pine together with scattered individuals of European larch and Silver birch (Betula pendula Roth.). The involved workflow used canopy height models (CHMs) for the extraction of height, the smoothing of raster images for the determination of the local maxima, and Inverse Watershed Segmentation (IWS) for the estimation of the crown diameters with the help of a geographical information system (GIS). Finally, we validated the accuracies of the two methods by comparing the UAV results with ground measurements. The results showed higher agreement between field and remote-sensed data for heights than for crown diameters based on RMSE%, which were in the range 11.42–12.62 for height and 14.29–18.56 for crown diameter. Overall, the accuracy of the results was acceptable and showed that the methods were feasible for detecting tree heights and crown diameter.

Accuracy of Reconstruction of the Tree Stem Surface Using Terrestrial Close-Range Photogrammetry

Airborne laser scanning (ALS) allows for extensive coverage, but the accuracy of tree detection and form can be limited. Although terrestrial laser scanning (TLS) can improve on ALS accuracy, it is rather expensive and area coverage is limited. Multi-view stereopsis (MVS) techniques combining computer vision and photogrammetry may offer some of the coverage benefits of ALS and the improved accuracy of TLS; MVS combines computer vision research and automatic analysis of digital images from common commercial digital cameras with various algorithms to reconstruct three-dimensional (3D) objects with realistic shape and appearance. Despite the relative accuracy (relative geometrical distortion) of the reconstructions available in the processing software, the absolute accuracy is uncertain and difficult to evaluate. We evaluated the data collected by a common digital camera through the processing software (Agisoft PhotoScan ©) for photogrammetry by comparing those by direct measurement of the 3D magnetic motion tracker. Our analyses indicated that the error is mostly concentrated in the portions of the tree where visibility is lower, i.e., the bottom and upper parts of the stem. For each reference point from the digitizer we determined how many cameras could view this point. With a greater number of cameras we found increasing accuracy of the measured object space point positions (as expected), with a significant positive change in the trend beyond five cameras; when more than five cameras could view this point, the accuracy began to increase more abruptly, but eight cameras or more provided no increases in accuracy. This method allows for the retrieval of larger datasets from the measurements, which could improve the accuracy of estimates of 3D structure of trees at potentially reduced costs.

Constructing tree stem form from digitized surface measurements by a programming approach within discrete mathematics

Key messageThe main message of this work is the demonstration of possibility of creation of stem shape from digitized points using integer-programming approach. The points are digitized by magnetic motion tracker which in contrast to the laser scanning allows the reconstruction of complete cross-section of stem even in the “hidden (invisible)” part.AbstractThree-dimensional information on tree stem form plays an important role in understanding the structure and strength of a standing tree against the forces of wind, snow, and other natural pressure. It also contributes to precision in volume measurement compared to conventional two-dimensional measurement. We investigate approaches for obtaining three-dimensional information of tree stem form from partially organized surface measurements, acquired using a three-dimensional digitizing device (Polhemus FASTRAK® motion tracking device). We then propose a new programming approach from discrete mathematics to construct tree stem form. Our method is based on an optimal connection of neighbor triangles for surface construction, which is created by locally possible combination of three digitized points on the stem surface. We compare the proposed method to the existing heuristic methods of contour tracing and region growing. Our analysis shows that the proposed method provides a consistent construction of tree stem form, for even stems with extremely irregular structure such as those from bent trees and mangrove trees with unique root spread, while the other methods are incapable for constructing such tree stems.

Importance of automatic threshold for image segmentation for accurate measurement of fine roots of woody plants / Význam automatického prahovania na obrazovú segmentáciu pre presné merania jemných koreňov drevín

Abstract The fine roots are considered the key organs for plant survival, growth and productivity. Measurement of fine roots variables is easily and conveniently achieved by means of digital image. The descriptive variables like root area, surface, total length and diameter distribution may be obtained from the image. Analysis of digital image consists from several steps, each of them represents potential source of the error. In this article we want to evaluate the automatic thresholding and its impact on principal variables obtainable from digital scans of the fine roots. We compare 16 different thresholding methods and compare them with the human processed binary images of roots of cork oak (Quercus suber L.). We found some of the thresholding methods perform significantly better than others in the estimation of total projected area however the length estimation error points out a little different order of accuracy Abstrakt Jemné korene sú považované za kľúčové orgány zabezpečujúce prežitie rastliny, jej rast a produkciu. Merania jemných koreňov sú ľahko a pohodlne vykonávateľné pomocou digitálneho obrazu. Popisné veličiny ako plocha koreňov, ich povrch, celková dĺžka či hrúbková štruktúra sa dajú získať z digitálneho obrazu. Analýza digitálneho obrazu pozostáva z niekoľkých krokov, z ktorých každý predstavuje potenciálny zdroj chyby merania. V tomto článku sa zameriavame na automatickú segmentáciu prahovaním a jej vplyv na veličiny získavané z digitálnych skenov jemných koreňov korkového duba (Quercus suber L.). Porovnávame 16 rôznych prahovacích metód a ich správnosť v porovnaní s binárnymi obrazmi vytvorenými ľudským hodnotiteľom. Zistili sme, že niektoré prahovacie metódy dávajú lepšie výsledky ako ostatné v odhade plochy koreňov, ale pred odhad dĺžky koreňov je poradie metód podľa ich správnosti len mierne odlišné

Estimation of positions and heights from UAV-sensed imagery in tree plantations in agrosilvopastoral systems

ABSTRACT Plantations of typical Mediterranean tree species, such as cork oak(Quercus suber L.), holm oak (Quercus ilex L.), and umbrella pine (Pinus pinea L.), are important for the restoration of forest ecosystems in the region. While traditional forest inventories can provide early problem detection in these plantations, the cost and labour of the required fieldwork may exceed its potential benefits. Unmanned aerial vehicles (UAVs) provide a cheap and practical alternative to traditional inventories and individual tree measurement. We present a method to estimate heights and positions of individual trees, from remotely sensed imagery, obtained using a low-flying UAV with an integrated RGB sensor. In the summer of 2015, a 5 ha stand at the University of Évora was photographed with a low-flying (40 m) hexacopter. A 3D point cloud and orthophoto were created from the images. The point cloud was used to identify local maxima as candidates for tree positions and height estimates. Results showed that the height measured with the UAV was reliable on pines, whereas the reliability for oaks was dependent on the size of the trees: smaller trees were especially problematic as they tended to have an irregular crown shape, resulting in larger errors. However, the error showed a strong trend, and adequate models could be produced to improve the estimates.

Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands

Abstract The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas. The objectives are: (1) to test the applicability of UAV visible an near-infrared (VNIR) and geometrical data based on Z values of point dense cloud (PDC) raster to separate forest species and dead trees in the study area; (2) to explore the relationship between UAV VNIR data and individual spruce health indicators from field sampling; and (3) to explore the possibility of the qualitative classification of spruce health indicators. Analysis based on NDVI and PDC raster was successfully applied for separation of spruce and silver fir, and for identification of dead tree category. Separation between common beech and fir was distinguished by the object-oriented image analysis. NDVI was able to identify the presence of key indicators of spruce health, such as mechanical damage on stems and stem resin exudation linked to honey fungus infestation, while stem damage by peeling was identified at the significance margin. The results contributed to improving separation of coniferous (spruce and fir) tree species based on VNIR and PDC raster UAV data, and newly demonstrated the potential of NDVI for qualitative classification of spruce trees. The proposed methodology can be applicable for monitoring of spruce health condition in the local forest sites.

Automatic detection and quantification of wild game crop damage using an unmanned aerial vehicle (UAV) equipped with an optical sensor payload: a case study in wheat

ABSTRACT Wildlife-induced damage of agricultural crops is an unfavorable consequence of elevated population densities of wild animals, especially wild boars. For the purposes of financial compensations for crop damage, provided by either governments or hunters responsible for game numbers, it is necessary to precisely assess the range of damage and temporal change. The use of an unmanned aerial vehicle (UAV) with an optical sensor payload represents a potential method of obtaining data of crop conditions without the necessity to enter the field and increase the damage. We propose a novel method for delineation of damaged areas via automatic segmentation of the crop field. Our method is based on photogrammetric reconstruction of the various crop heights within the field through the use of Structure from Motion technique with subsequent automatic classification. In this case study of wheat, the range of damage was estimated with an accuracy of 99.5% and 99.3% using field global navigation satellite system (GNSS) measurements and classification of an orthomosaic generated from UAV-based imagery, respectively.

Accuracy of Structure from Motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviour.

With the advantage of Structure from Motion technique, we reconstructed three-dimensional structures from two-dimensional image sequences in a circular plot with a radius of 6 m. The main objective of this research was to clarify the potential of using a low cost hand-held camera for evaluation of the stem accuracy reconstruction, through the comparison of data from two different point clouds. The first cloud comprises data collected with a digital camera that are compared with those collected by direct measurement of the FARO® Focus3D S120 laser scanner. Photos were taken in a circular plot of pine trees using the stop-and-go method. We estimated the Euclidean distance for corresponding points for both clouds and we found out that most of the points with error less than 11 cm are concentrated mainly on the ground. Regression analysis showed a significant relationship between height above ground and error, the error is more pronounced for points located higher on the stems. As expected, no dependence was found between the error of the points and the diameter at breast height of their respective stems.

Mapping Forest Structure Using UAS inside Flight Capabilities

We evaluated two unmanned aerial systems (UASs), namely the DJI Phantom 4 Pro and DJI Mavic Pro, for 3D forest structure mapping of the forest stand interior with the use of close-range photogrammetry techniques. Assisted flights were performed within two research plots established in mature pure Norway spruce (Picea abies (L.) H. Karst.) and European beech (Fagus sylvatica L.) forest stands. Geotagged images were used to produce georeferenced 3D point clouds representing tree stem surfaces. With a flight height of 8 m above the ground, the stems were precisely modeled up to a height of 10 m, which represents a considerably larger portion of the stem when compared with terrestrial close-range photogrammetry. Accuracy of the point clouds was evaluated by comparing field-measured tree diameters at breast height (DBH) with diameter estimates derived from the point cloud using four different fitting methods, including the bounding circle, convex hull, least squares circle, and least squares ellipse methods. The accuracy of DBH estimation varied with the UAS model and the diameter fitting method utilized. With the Phantom 4 Pro and the least squares ellipse method to estimate diameter, the mean error of diameter estimates was −1.17 cm (−3.14%) and 0.27 cm (0.69%) for spruce and beech stands, respectively.

UAV Capability to Detect and Interpret Solar Radiation as a Potential Replacement Method to Hemispherical Photography

Solar radiation is one of the most significant environmental factors that regulates the rate of photosynthesis, and consequently, growth. Light intensity in the forest can vary both spatially and temporally, so precise assessment of canopy and potential solar radiation can significantly influence the success of forest management actions, for example, the establishment of natural regeneration. In this case study, we investigated the possibilities and perspectives of close-range photogrammetric approaches for modeling the amount of potential direct and diffuse solar radiation during the growing seasons (spring–summer), by comparing the performance of low-cost Unmanned Aerial Vehicle (UAV) RGB imagery vs. Hemispherical Photography (HP). Characterization of the solar environment based on hemispherical photography has already been widely used in botany and ecology for a few decades, while the UAV method is relatively new. Also, we compared the importance of several components of potential solar irradiation and their impact on the regeneration of Pinus sylvestris L. For this purpose, a circular fisheye objective was used to obtain hemispherical images to assess sky openness and direct/diffuse photosynthetically active flux density under canopy average for the growing season. Concerning the UAV, a Canopy Height Model (CHM) was constructed based on Structure from Motion (SfM) algorithms using Photoscan professional. Different layers such as potential direct and diffuse radiation, direct duration, etc., were extracted from CHM using ArcGIS 10.3.1 (Esri: California, CA, USA). A zonal statistics tool was used in order to extract the digital data in tree positions and, subsequently, the correlation between potential solar radiation layers and the number of seedlings was evaluated. The results of this study showed that there is a high relation between the two used approaches (HP and UAV) with R2 = 0.74. Finally, potential diffuse solar radiation derived from both methods had the highest significant relation (−8.06% bias) and highest impact in the modeling of pine regeneration.