TY - GEN A1 - Brieger, Frederic A1 - Herzschuh, Ulrike A1 - Pestryakova, Luidmila Agafyevna A1 - Bookhagen, Bodo A1 - Zakharov, Evgenii S. A1 - Kruse, Stefan T1 - Advances in the derivation of Northeast Siberian forest metrics using high-resolution UAV-based photogrammetric point clouds T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1337 KW - UAV KW - photogrammetry KW - remote sensing KW - structure from motion KW - tundra–taiga ecotone KW - point cloud KW - forest structure Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-473318 SN - 1866-8372 IS - 1337 ER - TY - JOUR A1 - Pereira, Bruno A1 - Medeiros, Pedro Henrique Augusto A1 - Francke, Till A1 - Ramalho, Geraldo A1 - Förster, Saskia A1 - De Araujo, Jose Carlos T1 - Assessment of the geometry and volumes of small surface water reservoirs by remote sensing in a semi-arid region with high reservoir density JF - Hydrological sciences journal = Journal des sciences hydrologiques N2 - Water fluxes in highly impounded regions are heavily dependent on reservoir properties. However, for large and remote areas, this information is often unavailable. In this study, the geometry and volume of small surface reservoirs in the semi-arid region of Brazil were estimated using terrain and shape attributes extracted by remote sensing. Regression models and data classification were used to predict the volumes, at different water stages, of 312 reservoirs for which topographic information is available. The power function used to describe the reservoir shapes tends to overestimate the volumes; therefore, a modified shape equation was proposed. Among the methods tested, four were recommended based on performance and simplicity, for which the mean absolute percentage errors varied from 24 to 39%, in contrast to the 94% error achieved with the traditional method. Despite the challenge of precisely deriving the flooded areas of reservoirs, water management in highly reservoir-dense environments should benefit from volume prediction based on remote sensing. KW - reservoir volume KW - high-density reservoir network KW - water height-area-volume curve KW - semi-arid KW - remote sensing Y1 - 2019 U6 - https://doi.org/10.1080/02626667.2019.1566727 SN - 0262-6667 SN - 2150-3435 VL - 64 IS - 1 SP - 66 EP - 79 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - Brieger, Frederic A1 - Herzschuh, Ulrike A1 - Pestryakova, Luidmila Agafyevna A1 - Bookhagen, Bodo A1 - Zakharov, Evgenii S. A1 - Kruse, Stefan T1 - Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds JF - Remote sensing N2 - Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure. KW - UAV KW - photogrammetry KW - remote sensing KW - structure from motion KW - tundra-taiga ecotone KW - point cloud KW - forest structure Y1 - 2019 U6 - https://doi.org/10.3390/rs11121447 SN - 2072-4292 VL - 11 IS - 12 PB - MDPI CY - Basel ER - TY - JOUR A1 - Loibl, David A1 - Bookhagen, Bodo A1 - Valade, Sebastien A1 - Schneider, Christoph T1 - OSARIS, the "Open Source SAR Investigation System" for Automatized Parallel InSAR Processing of Sentinel-1 Time Series Data With Special Emphasis on Cryosphere Applications JF - Frontiers in Earth Science N2 - With the advent of the two Sentinel-1 (S1) satellites, Synthetic Aperture Radar (SAR) data with high temporal and spatial resolution are freely available. This provides a promising framework to facilitate detailed investigations of surface instabilities and movements on large scales with high temporal resolution, but also poses substantial processing challenges because of storage and computation requirements. Methods are needed to efficiently detect short term changes in dynamic environments. Approaches considering pair-wise processing of a series of consecutive scenes to retain maximum temporal resolution in conjunction with time series analyses are required. Here we present OSARIS, the “Open Source SAR Investigation System,” as a framework to process large stacks of S1 data on high-performance computing clusters. Based on Generic Mapping Tools SAR, shell scripts, and the workload manager Slurm, OSARIS provides an open and modular framework combining parallelization of high-performance C programs, flexible processing schemes, convenient configuration, and generation of geocoded stacks of analysis-ready base data, including amplitude, phase, coherence, and unwrapped interferograms. Time series analyses can be conducted by applying automated modules to the data stacks. The capabilities of OSARIS are demonstrated in a case study from the northwestern Tien Shan, Central Asia. After merging of slices, a total of 80 scene pairs were processed from 174 total input scenes. The coherence time series exhibits pronounced seasonal variability, with relatively high coherence values prevailing during the summer months in the nival zone. As an example of a time series analysis module, we present OSARIS' “Unstable Coherence Metric” which identifies pixels affected by significant drops from high to low coherence values. Measurements of motion provided by LOSD measurements require careful evaluation because interferometric phase unwrapping is prone to errors. Here, OSARIS provides a series of modules to detect and mask unwrapping errors, correct for atmospheric disturbances, and remove large-scale trends. Wall clock processing time for the case study (area ~9,000 km2) was ~12 h 4 min on a machine with 400 cores and 2 TB RAM. In total, ~12 d 10 h 44 min (~96%) were saved through parallelization. A comparison of selected OSARIS datasets to results from two state-of-the-art SAR processing suites, ISCE and SNAP, shows that OSARIS provides products of competitive quality despite its high level of automatization. OSARIS thus facilitates efficient S1-based region-wide investigations of surface movement events over multiple years. KW - remote sensing KW - InSAR KW - high mountain environments KW - rock glacier KW - sentinel-1 KW - time series analysis Y1 - 2019 U6 - https://doi.org/10.3389/feart.2019.00172 SN - 2296-6463 VL - 7 PB - Frontiers Media CY - Lausanne ER -