Browsing by Author "Seifert, Stefan"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- ItemDevelopment and validation of a photo-based measurement system to calculate the debarking percentages of processed logs(MDPI, 2019) Heppelmann, Joachim B.; Labelle, Eric R.; Seifert, Thomas; Seifert, Stefan; Wittkopf, StefanENGLISH ABSTRACT: Within a research project investigating the applicability and performance of modified harvesting heads used during the debarking of coniferous tree species, the actual debarking percentage of processed logs needed to be evaluated. Therefore, a computer-based photo-optical measurement system (Stemsurf) designed to assess the debarking percentage recorded in the field was developed, tested under laboratory conditions, and applied in live field operations. In total, 1720 processed logs of coniferous species from modified harvesting heads were recorded and analyzed within Stemsurf. With a single log image as the input, the overall debarking percentage was calculated by further estimating the un-displayed part of the log surface by defining polygons representing the differently debarked areas of the log surface. To assess the precision and bias of the developed measurement system, 480 images were captured under laboratory conditions on an artificial log with defined surface polygons. Within the laboratory test, the standard deviation of average debarking percentages remained within a 4% variation. A positive bias of 6.7% was caused by distortion and perspective effects. This resulted in an average underestimation of 1.1% for the summer debarking percentages gathered from field operations. The software generally performed as anticipated through field and lab testing and offered a suitable alternative of assessing stem debarking percentage, a task that should increase in importance as more operations are targeting debarked products.
- ItemInfluence of drone altitude, image overlap, and optical sensor resolution on multi-view reconstruction of forest images(MDPI, 2019) Seifert, Erich; Seifert, Stefan; Vogt, Holger; Drew, David; Van Aardt, Jan; Kunneke, Anton; Seifer, ThomasRecent technical advances in drones make them increasingly relevant and important toolsfor forest measurements. However, information on how to optimally set flight parameters and choosesensor resolution is lagging behind the technical developments. Our study aims to address this gap,exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) onimage reconstruction and successful 3D point extraction. This study was conducted using video footageobtained from flights at several altitudes, sampled for images at varying frequencies to obtain forwardoverlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulatesensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed usingcommercial multi-view reconstruction (MVG) software to understand the effects of drone variables on(1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times duringdata processing. The correlations between variables were statistically analysed with a multivariategeneralised additive model (GAM), based on a tensor spline smoother to construct response surfaces.Flight time was linearly related to altitude, while processing time was mainly influenced by altitudeand forward overlap, which in turn changed the number of images processed. Low flight altitudesyielded the highest reconstruction details and best precision, particularly in combination with high imageoverlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution athigher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithmto identify more points on the silhouettes of tree crowns. Our results are some of the first estimates ofreasonable value ranges for flight parameter selection for forestry applications.