Sledování sukcese dřevin po požáru s využitím leteckých snímků na lokalitě Havraní skála v NP České Švýcarsko

This work explores the expansion of natural regeneration after the forest fire in the National Park Bohemian Switzerland, which occured in June 2006. Aerial photography were acquired in years 2009 and 2011 and both were used. The resulting accuracy achieved in the natural regeneration classification was 96 % in the year 2009 and 94 % in the year 2011. Natural regeneration covered 14% of the territory in year 2009 and in the year 2011 it was 51%. Furthermore comparison between the coverage of the natural regeneration and amount of individuals on the permanent research areas was made. The resulting correlation coefficient and coefficient of determination were low. The reason is, that the natural regeneration coverge detected using remote sensing is affected by dry crowns, high diversity of natural regeneration and also the absolute positional accuracy of aerial images

Drone LiDAR remote sensing for mistletoe recognition and monitoring

1043003023Ministry of Education Youth and Sports of the Czech Republi

Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees

We applied a supervised individual-tree segmentation algorithm to ultra-high-density drone lidar in a temperate mountain forest in the southern Czech Republic. We compared the number of trees correctly segmented, stem diameter at breast height (DBH), and tree height from drone-lidar segmentations to field-inventory measurements and segmentations from terrestrial laser scanning (TLS) data acquired within two days of the drone-lidar acquisition. Our analysis detected 51% of the stems >15 cm DBH, and 87% of stems >50 cm DBH. Errors of omission were much more common for smaller trees than for larger ones, and were caused by removal of points prior to segmentation using a low-intensity and morphological filter. Analysis of segmented trees indicates a strong linear relationship between DBH from drone-lidar segmentations and TLS data. The slope of this relationship is 0.93, the intercept is 4.28 cm, and the r2 is 0.98. However, drone lidar and TLS segmentations overestimated DBH for the smallest trees and underestimated DBH for the largest trees in comparison to field data. We evaluate the impact of random error in point locations and variation in footprint size, and demonstrate that random error in point locations is likely to cause an overestimation bias for small-DBH trees. A Random Forest classifier correctly identified broadleaf and needleleaf trees using stem and crown geometric properties with overall accuracy of 85.9%. We used these classifications and DBH estimates from drone-lidar segmentations to apply allometric scaling equations to segmented individual trees. The stand-level aboveground biomass (AGB) estimate using these data is 76% of the value obtained using a traditional field inventory. We demonstrate that 71% of the omitted AGB is due to segmentation errors of omission, and the remaining 29% is due to DBH estimation errors. Our analysis indicates that high-density measurements from low-altitude drone flight can produce DBH estimates for individual trees that are comparable to TLS. These data can be collected rapidly throughout areas large enough to produce landscape-scale estimates. With additional refinement, these estimates could augment or replace manual field inventories, and could support the calibration and validation of current and forthcoming space missions

3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR

<div><p>Terrestrial laser scanning is a powerful technology for capturing the three-dimensional structure of forests with a high level of detail and accuracy. Over the last decade, many algorithms have been developed to extract various tree parameters from terrestrial laser scanning data. Here we present 3D Forest, an open-source non-platform-specific software application with an easy-to-use graphical user interface with the compilation of algorithms focused on the forest environment and extraction of tree parameters. The current version (0.42) extracts important parameters of forest structure from the terrestrial laser scanning data, such as stem positions (<i>X</i>, <i>Y</i>, <i>Z</i>), tree heights, diameters at breast height (DBH), as well as more advanced parameters such as tree planar projections, stem profiles or detailed crown parameters including convex and concave crown surface and volume. Moreover, 3D Forest provides quantitative measures of between-crown interactions and their real arrangement in 3D space. 3D Forest also includes an original algorithm of automatic tree segmentation and crown segmentation. Comparison with field data measurements showed no significant difference in measuring DBH or tree height using 3D Forest, although for DBH only the Randomized Hough Transform algorithm proved to be sufficiently resistant to noise and provided results comparable to traditional field measurements.</p></div