@article{RederMundAlbertetal.2021, author = {Reder, Stefan and Mund, Jan-Peter and Albert, Nicole and Waßermann, Lilli and Miranda, Luis}, title = {Detection of Windthrown Tree Stems on UAV-Orthomosaics Using U-Net Convolutional Networks}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {1}, publisher = {MDPI}, issn = {2072-4292}, doi = {10.3390/rs14010075}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-2213}, pages = {25}, year = {2021}, abstract = {The increasing number of severe storm events is threatening European forests. Besides the primary damages directly caused by storms, there are secondary damages such as bark beetle outbreaks and tertiary damages due to negative effects on the market. These subsequent damages can be minimized if a detailed overview of the affected area and the amount of damaged wood can be obtained quickly and included in the planning of clearance measures. The present work utilizes UAV-orthophotos and an adaptation of the U-Net architecture for the semantic segmentation and localization of windthrown stems. The network was pre-trained with generic datasets, randomly combining stems and background samples in a copy-paste augmentation, and afterwards trained with a specific dataset of a particular windthrow. The models pre-trained with generic datasets containing 10, 50 and 100 augmentations per annotated windthrown stems achieved F1-scores of 73.9\% (S1Mod10), 74.3\% (S1Mod50) and 75.6\% (S1Mod100), outperforming the baseline model (F1-score 72.6\%), which was not pre-trained. These results emphasize the applicability of the method to correctly identify windthrown trees and suggest the collection of training samples from other tree species and windthrow areas to improve the ability to generalize. Further enhancements of the network architecture are considered to improve the classification performance and to minimize the calculative costs.}, language = {en} } @inproceedings{deMiguelDiezRederWalloretal.2021, author = {de Miguel-D{\´i}ez, Felipe and Reder, Stefan and Wallor, Evelyn and Bahr, Henrik and Mund, Jan-Peter and Cremer, Tobias}, title = {Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology in Roundwood Measurement}, series = {Proceedings of the SilviLaser Conference 2021}, booktitle = {Proceedings of the SilviLaser Conference 2021}, doi = {10.34726/wim.1929}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-4462}, pages = {3}, year = {2021}, language = {en} } @article{deMiguelDiezRederWalloretal.2022, author = {de Miguel-D{\´i}ez, Felipe and Reder, Stefan and Wallor, Evelyn and Bahr, Henrik and Blasko, Lubomir and Mund, Jan-Peter and Cremer, Tobias}, title = {Further application of using a personal laser scanner and simultaneous localization and mapping technology to estimate the log's volume and its comparison with traditional methods}, series = {International Journal of Applied Earth Observation and Geoinformation}, journal = {International Journal of Applied Earth Observation and Geoinformation}, number = {109}, publisher = {Elsevier}, issn = {1872-826X}, doi = {10.1016/j.jag.2022.102779}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-4496}, pages = {7}, year = {2022}, abstract = {The aim of this study was the development of a methodical processing line for estimating the log's volume from scanning logs using a long-range high-speed personal laser scanner (PLS) GeoSLAM ZEB HORIZON and simultaneous localization and mapping (SLAM) technology in an open-source software framework of Cloud Compare. Additionally, the accuracy and precision of using PLS and the suggested method when measuring roundwood volume was examined compared to measurements made using a xylometer and estimations obtained from applying the formulae of Huber, Smalian and Newton. For this purpose, several parameters were measured in 50 logs of Norway spruce with an average length of 2.53 m and a mean diameter of 19.97 cm. Afterwards, the volume of these 50 logs was measured with a xylometer. The results of these measurements served as reference values. The same 50 logs were subsequently scanned with the PLS. The scans were converted into point clouds and were analyzed in Cloud Compare to estimate the volume of the scanned logs. Next, the root mean square error (RMSE) and the mean bias error (MBE) as well as their relative values were calculated for the volumes determined in Cloud Compare and estimated with the above mentioned formulae. The calculated RMSE (and relative RMSE) determined a deviation of the logs' volumes estimated in Cloud Compare from the xylometric volumes of 2.88 dm3 (3.54\%) whereas the deviations of the log's volumes calculated applying the formulae of Huber, Smalian and Newton from the xylometric volumes were respectively 9.63 dm3 (11.83\%), 10.33 dm3 (12.69\%) and 4.69 dm3 (5.76\%). The calculated MBE (and relative MBE) showed that the estimated volumes in Cloud Compare and those using the formulae were overestimated, with the lowest overestimation of 0.10 dm3 (0.12\%) in Cloud Compare and the highest, 5.50 dm3 (6.75\%), using the Smalian formula. Therefore, it can be stated that results of suggested methodical processing line came closest to the logs' volumes obtained with the xylometer, i.e., they were more accurate and precise compared to the conventionally formulae for log's volume estimation. The implementation of this method has the potential filling a gap towards a wall-to-wall complete digitization of the roundwood commercialization and ensure transparency and acceptance between the stakeholders involved in the wood supply chain.}, language = {en} } @masterthesis{RederWassermann2019, type = {Bachelor Thesis}, author = {Reder, Stefan and Waßermann, Lilli}, title = {UAV-based Analysis of Canopy Structures in Rainforest Areas in Ecuador and Brazil}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-8916}, school = {Hochschule f{\"u}r nachhaltige Entwicklung Eberswalde}, year = {2019}, abstract = {With the development of new imaging and near field remote sensing UAV technologies, the field of forest canopy and tree crown investigations in the context of rainforest ecology gained new perspectives in recent years. This study demonstrates the feasibility of structural analysis of canopy for the assessment of succession states of tropical lowland rainforests using a consumer UAV and open access ground reference data. Therefore, the canopy of 22 small-scale forest plots in the Ecuadorian Andes and the Mata Atl{\^a}ntica were captured with an UAV between October 2017 and February 2018. The fully remote image acquisition used automated flight plans consisting of crossed flights with different incident camera angles and did not require ground control points (GCP) as further ground reference. Photogrammetric Point Clouds (PPC), image meshes and crown height models (CHM) were created with Structure from Motion (SfM), combined with a Digital Terrain Model from the Shuttle Radar Topography Mission (SRTM), in order to analyze structural parameters of the canopy. The standard deviation of the height values of the PPC and the CHM differs statistically significant between succession stages. Investigation plots in climax or late succession show a mean PPCSD of 6.7 m with a standard error of 0,3 m. Plots in early succession show a PPCSD of 4.8 m with a standard error of 0.3 m and degraded plots show 2.8m with a standard error of 0.2m. This result proves the applicability of the proposed workflow under typical NGO conditions and other applied science research with limitations in budget and access to sophisticated survey equipment.}, language = {en} } @misc{Reder2023, type = {Master Thesis}, author = {Reder, Stefan}, title = {Detection and quantification of windthrown tree stems on UAV-orthomosaics by deep learning}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-8909}, school = {Hochschule f{\"u}r nachhaltige Entwicklung Eberswalde}, year = {2023}, abstract = {Severe storm events are the biggest driving factor for biomass loss in European forests. Besides the damages caused by the storm itself, there are subsequent damages due to biotic, abiotic and market factors with far reaching implications for forestry and conservation. These subsequent damages can be minimized if the amount and spatial distribution of the windthrown trees is known and can be used to optimize salvage operations and calamity management. Traditional methods and space born remote sensing can only provide estimations of the affected area, whereas remote sensing with aerial sensors is able to obtain the spatial distribution of the stems with detection rates up to 92\% but not to quantify single logs due to an insufficient spatial resolution. Recent approaches utilizing UAVs are promising but a methodology for the quantification of windthrown trees is not published yet. The presented work closes this gap by analyzing UAV-orthomosaics with Deep Learning techniques to obtain precise information of the spatial distribution and estimate the volume of the windthrown trees. Therefore, the U-Net was included in a bottom-up object detection based on a skeletonization algorithm including a reconstruction of occluded stem parts by a voting system based on morphological heuristics. For the subsequent quantification of the detected stems, the diameter is determined every 25 cm and the volume is calculated as sum of truncated cone volumes. In the scope of this work, 21 orthomosaics of beech, spruce and mixed stands with a ground sampling distance of mostly less than 2 cm were used, on which 1747 windthrown tree stems were manually outlined for the training of the models "Spruce", "Beech" and "General" for pure and mixed stands, respectively. Additionally, 710 trees were digitized and measured for the validation of the methodology. It could be proven that the proposed methodology is able to detect windthrown tree stems with an average detection rate of 93.8\% for spruce stands and with 93.3\% for beech and mixed stans (error rates between 4.3\% and 6.3\%). Thereby, the detected volumes were overestimated by the specific models for spruce (11.8\%) and beech stands (10.7\%) while the "General" model was underestimating the volume by 5.8\%, on average. Generally, specialization on certain tree species carries the risk of lower detection rates for unfamiliar scenes and species, while a general model is associated with a higher rate of classification errors. Further, it could be shown that the performance of the proposed methodology is affected by the quality of the used orthomosaics. The quantification of the amount of windthrown wood and the reconstruction of occluded stem parts are unique features compared to other recently published approaches with far reaching implications for forestry, ecology, and biodiversity conservation. The proposed methodology provides additional information for decision-making in the planning of salvage loggings and for monitoring of biomass and carbon cycles. Further, it can contribute to a better understanding of windthrow dynamics and therewith, will support the development of sustainable management strategies which focus on resilient forest ecosystems.}, subject = {windthrow}, language = {en} }