Study of pine forest stand structure in the priosko-terrasny state nature biosphere reserve (Russia) based on aerial photography by Quadrocopter

Abstract

In this paper, we investigated three pine (Pinus sylvestris) forest plots (each of 50 × 50 m), different by age and composition, located in the Prioksko-Terrasny State Nature Biosphere Reserve (Moscow Region, Russia). This study was aimed to evaluate the forest stand attributes based on the photogrammetricpoint clouds and canopy height models (CHM). Foraerialphotography, we used the unmanned aerialvehicle (UAV) quadrocopterDJI Phantom 4. At the first step, we used Agisoft Metashape software forthe building of dense photogrammetricpoint clouds and orthophotoplans. Then we used the lidR package in the Renvironment forprocessing of dense point clouds. We used a cloth simulation filterforclassification of ground points, spatialinterpolation algorithm tin forcreating a normalised dataset, and the algorithm lmf (localmaximum filter) forindividualtree detection and tree height assessment. Foraccuracy assessment, we collected field-based data, and calculated recall(r), precision (p), and F-score (F). Finally, we calculated CHMs (30 cm/pixel) derived from dense point clouds using the pit-free algorithm. To address the value of UAV data fordelineating tree crowns, we compared the outputs of CHM data using two common algorithms (watershed and region-growing), and the result of manualorthophotoplans vectorisation. We obtained a high accuracy of individualtree detection. The algorithm found 46.7% to 87.5% of trees accounted on the sample plots by the field-based surveys. The recall(r) value varied from 0.5 to 0.9. The value of p varied from 0.9 to 1.0. The F-score, considering both factors (p and r), varied from 0.7 to 0.9. The highest accuracy was obtained in the site with a single-layerstand, where large trees with well-distinct tree crowns dominated. Spatialheterogeneity of tree stands reduces the accuracy of tree detection. We also found that tree heights estimated on the dense clouds were wellmatched with tree heights measured in the field. This dependency was described by the linearregression of y = 0.99x, R2 = 0.99. With both the watershed and region-growing algorithms, the totalcrown area estimation often exceeded the results of orthophotoplans manualvectorisation, where differences reached 25.1%. Differences between two delineation algorithms varied from 0.2% to 19.7% forthe same sites. More accurate results were obtained forplots with lesserdensity of tree stands. Overall, ourresults have shown the potentialof using photogrammetricpoint clouds forestimating tree attributes (heights and density) in single-layerpine stands. Widely used tree crown segmentation algorithms do not provide reliable estimates of the crown projection area, and more accurate results could be obtained afterfurtherimprovement of the technique.