- Article
(2952 KB) - Metadata XML
- July 10, 2025
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
- |
- 𝕏
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-2/W2-2022
https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
08 Dec 2022
| 08 Dec 2022
CHARACTERIZATION OF MORPHOLOGICAL SURFACE ACTIVITIES DERIVED FROM NEAR-CONTINUOUS TERRESTRIAL LIDAR TIME SERIES
D. Hulskemper
Dept. of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geoscience, Delft University of Technology, The Netherlands
3DGeo Research Group, Institute of Geography, Heidelberg University, Germany
K. Anders
3DGeo Research Group, Institute of Geography, Heidelberg University, Germany
J. A. Á. Antolínez
Dept. of Hydraulic Engineering, Faculty of Civil Engineering and Geoscience, Delft University of Technology, The Netherlands
M. Kuschnerus
Dept. of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geoscience, Delft University of Technology, The Netherlands
3DGeo Research Group, Institute of Geography, Heidelberg University, Germany
R. Lindenbergh
Dept. of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geoscience, Delft University of Technology, The Netherlands
Related authors
No articles found.
Yushan Liu, Alireza Amiri-Simkooei, Roderik Lindenbergh, and Mirjam Snellen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W10-2025, 169–176, https://doi.org/10.5194/isprs-archives-XLVIII-2-W10-2025-169-2025, https://doi.org/10.5194/isprs-archives-XLVIII-2-W10-2025-169-2025, 2025
Lucas Terlinden-Ruhl, Anaïs Couasnon, Dirk Eilander, Gijs G. Hendrickx, Patricia Mares-Nasarre, and José A. Á. Antolínez
Nat. Hazards Earth Syst. Sci., 25, 1353–1375, https://doi.org/10.5194/nhess-25-1353-2025, https://doi.org/10.5194/nhess-25-1353-2025, 2025
Short summary
Short summary
This study develops a conceptual framework that uses active learning to accelerate compound flood risk assessments. A case study of Charleston County shows that the framework achieves faster and more accurate risk quantification compared to the state-of-the-art. This win–win allows for an increase in the number of flooding parameters, which results in an 11.6 % difference in the expected annual damages. Therefore, this framework allows for more comprehensive compound flood risk assessments.
Robert McCall, Curt Storlazzi, Floortje Roelvink, Stuart G. Pearson, Roel de Goede, and José A. Á. Antolínez
Nat. Hazards Earth Syst. Sci., 24, 3597–3625, https://doi.org/10.5194/nhess-24-3597-2024, https://doi.org/10.5194/nhess-24-3597-2024, 2024
Short summary
Short summary
Accurate predictions of wave-driven flooding are essential to manage risk on low-lying, reef-lined coasts. Models to provide this information are, however, computationally expensive. We present and validate a modeling system that simulates flood drivers on diverse and complex reef-lined coasts as competently as a full-physics model but at a fraction of the computational cost to run. This development paves the way for application in large-scale early-warning systems and flood risk assessments.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
Short summary
Short summary
Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
M. Potůčková, J. Albrechtová, K. Anders, L. Červená, J. Dvořák, K. Gryguc, B. Höfle, L. Hunt, Z. Lhotáková, A. Marcinkowska-Ochtyra, A. Mayr, E. Neuwirthová, A. Ochtyra, M. Rutzinger, A. Šedová, A. Šrollerů, and L. Kupková
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 989–996, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-989-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-989-2023, 2023
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745, https://doi.org/10.5194/nhess-23-3723-2023, https://doi.org/10.5194/nhess-23-3723-2023, 2023
Short summary
Short summary
Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if the use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such a machine learning system is not as straightforward as often hoped for.
Lukas Winiwarter, Katharina Anders, Daniel Czerwonka-Schröder, and Bernhard Höfle
Earth Surf. Dynam., 11, 593–613, https://doi.org/10.5194/esurf-11-593-2023, https://doi.org/10.5194/esurf-11-593-2023, 2023
Short summary
Short summary
We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.
Lea Hartl, Thomas Zieher, Magnus Bremer, Martin Stocker-Waldhuber, Vivien Zahs, Bernhard Höfle, Christoph Klug, and Alessandro Cicoira
Earth Surf. Dynam., 11, 117–147, https://doi.org/10.5194/esurf-11-117-2023, https://doi.org/10.5194/esurf-11-117-2023, 2023
Short summary
Short summary
The rock glacier in Äußeres Hochebenkar (Austria) moved faster in 2021–2022 than it has in about 70 years of monitoring. It is currently destabilizing. Using a combination of different data types and methods, we show that there have been two cycles of destabilization at Hochebenkar and provide a detailed analysis of velocity and surface changes. Because our time series are very long and show repeated destabilization, this helps us better understand the processes of rock glacier destabilization.
J. P. Meinderts, R. Lindenbergh, D. H. van der Heide, A. Amiri-Simkooei, and L. Truong-Hong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W2-2022, 69–76, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-69-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-69-2022, 2022
Panagiotis Athanasiou, Ap van Dongeren, Alessio Giardino, Michalis Vousdoukas, Jose A. A. Antolinez, and Roshanka Ranasinghe
Nat. Hazards Earth Syst. Sci., 22, 3897–3915, https://doi.org/10.5194/nhess-22-3897-2022, https://doi.org/10.5194/nhess-22-3897-2022, 2022
Short summary
Short summary
Sandy dunes protect the hinterland from coastal flooding during storms. Thus, models that can efficiently predict dune erosion are critical for coastal zone management and early warning systems. Here we develop such a model for the Dutch coast based on machine learning techniques, allowing for dune erosion estimations in a matter of seconds relative to available computationally expensive models. Validation of the model against benchmark data and observations shows good agreement.
L. Truong-Hong, R. C. Lindenbergh, and M. J. Vermeij
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W4-2022, 161–168, https://doi.org/10.5194/isprs-archives-XLVIII-4-W4-2022-161-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W4-2022-161-2022, 2022
Hannah Weiser, Jannika Schäfer, Lukas Winiwarter, Nina Krašovec, Fabian E. Fassnacht, and Bernhard Höfle
Earth Syst. Sci. Data, 14, 2989–3012, https://doi.org/10.5194/essd-14-2989-2022, https://doi.org/10.5194/essd-14-2989-2022, 2022
Short summary
Short summary
3D point clouds, acquired by laser scanning, allow us to retrieve information about forest structure and individual tree properties. We conducted airborne, UAV-borne and terrestrial laser scanning in German mixed forests, resulting in overlapping point clouds with different characteristics. From these, we generated a comprehensive database of individual tree point clouds and corresponding tree metrics. Our dataset may serve as a benchmark dataset for algorithms in forestry research.
A. Nurunnabi, F. N. Teferle, R. C. Lindenbergh, J. Li, and S. Zlatanova
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 59–66, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022, 2022
K. Anders, L. Winiwarter, D. Schröder, and B. Höfle
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 973–980, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-973-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-973-2022, 2022
H. S. Kathmann, A. L. van Natijne, and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1033–1040, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1033-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1033-2022, 2022
M. Kuschnerus, R. Lindenbergh, Q. Lodder, E. Brand, and S. Vos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1055–1061, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1055-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1055-2022, 2022
V. Zahs, L. Winiwarter, K. Anders, M. Bremer, M. Rutzinger, M. Potůčková, and B. Höfle
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1109–1116, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, 2022
L. Winiwarter, K. Anders, D. Schröder, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 79–86, https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022, 2022
F. Dahle, J. Tanke, B. Wouters, and R. Lindenbergh
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 237–244, https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022, 2022
A. Nurunnabi, F. N. Teferle, D. F. Laefer, R. C. Lindenbergh, and A. Hunegnaw
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2-W1-2022, 401–408, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022, 2022
A. Nurunnabi, F. N. Teferle, J. Li, R. C. Lindenbergh, and S. Parvaz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W5-2021, 397–404, https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-397-2021, https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-397-2021, 2021
L. Truong-Hong, N. Nguyen, R. Lindenbergh, P. Fisk, and T. Huynh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W4-2021, 119–124, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-119-2021, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-119-2021, 2021
A. Nurunnabi, F. N. Teferle, J. Li, R. C. Lindenbergh, and A. Hunegnaw
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 31–38, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021, 2021
Q. Bai, R. C. Lindenbergh, J. Vijverberg, and J. A. P. Guelen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 115–122, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-115-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-115-2021, 2021
M. Kuschnerus, D. Schröder, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 745–752, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-745-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-745-2021, 2021
K. Anders, L. Winiwarter, H. Mara, R. C. Lindenbergh, S. E. Vos, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 137–144, https://doi.org/10.5194/isprs-annals-V-2-2021-137-2021, https://doi.org/10.5194/isprs-annals-V-2-2021-137-2021, 2021
Mieke Kuschnerus, Roderik Lindenbergh, and Sander Vos
Earth Surf. Dynam., 9, 89–103, https://doi.org/10.5194/esurf-9-89-2021, https://doi.org/10.5194/esurf-9-89-2021, 2021
Short summary
Short summary
Sandy coasts are areas that undergo a lot of changes, which are caused by different influences, such as tides, wind or human activity. Permanent laser scanning is used to generate a three-dimensional representation of a part of the coast continuously over an extended period. By comparing three unsupervised learning algorithms, we develop a methodology to analyse the resulting data set and derive which processes are dominating changes in the beach and dunes.
Veit Ulrich, Jack G. Williams, Vivien Zahs, Katharina Anders, Stefan Hecht, and Bernhard Höfle
Earth Surf. Dynam., 9, 19–28, https://doi.org/10.5194/esurf-9-19-2021, https://doi.org/10.5194/esurf-9-19-2021, 2021
Short summary
Short summary
In this work, we use 3D point clouds to detect topographic changes across the surface of a rock glacier. These changes are presented as the relative contribution of surface change during a 3-week period to the annual surface change. By comparing these different time periods and looking at change in different directions, we provide estimates showing that different directions of surface change are dominant at different times of the year. This demonstrates the benefit of frequent monitoring.
Bram C. van Prooijen, Marion F. S. Tissier, Floris P. de Wit, Stuart G. Pearson, Laura B. Brakenhoff, Marcel C. G. van Maarseveen, Maarten van der Vegt, Jan-Willem Mol, Frank Kok, Harriette Holzhauer, Jebbe J. van der Werf, Tommer Vermaas, Matthijs Gawehn, Bart Grasmeijer, Edwin P. L. Elias, Pieter Koen Tonnon, Giorgio Santinelli, José A. A. Antolínez, Paul Lodewijk M. de Vet, Ad J. H. M. Reniers, Zheng Bing Wang, Cornelis den Heijer, Carola van Gelder-Maas, Rinse J. A. Wilmink, Cor A. Schipper, and Harry de Looff
Earth Syst. Sci. Data, 12, 2775–2786, https://doi.org/10.5194/essd-12-2775-2020, https://doi.org/10.5194/essd-12-2775-2020, 2020
Short summary
Short summary
To protect the Dutch coastal zone, sand is nourished and disposed at strategic locations. Simple questions like where, how, how much and when to nourish the sand are not straightforward to answer. This is especially the case around the Wadden Sea islands where sediment transport pathways are complicated. Therefore, a large-scale field campaign has been carried out on the seaward side of Ameland Inlet. Sediment transport, hydrodynamics, morphology and fauna in the bed were measured.
M. Rutzinger, K. Anders, M. Bremer, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, M. Scaioni, and T. Zieher
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 243–250, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, 2020
E. Widyaningrum, M. K. Fajari, R. C. Lindenbergh, and M. Hahn
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 339–346, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-339-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-339-2020, 2020
L. Truong-Hong and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 501–506, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-501-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-501-2020, 2020
I. Gutierrez, E. Før Gjermundsen, W. D. Harcourt, M. Kuschnerus, F. Tonion, and T. Zieher
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 719–726, https://doi.org/10.5194/isprs-annals-V-2-2020-719-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-719-2020, 2020
L. Winiwarter, K. Anders, D. Wujanz, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 789–796, https://doi.org/10.5194/isprs-annals-V-2-2020-789-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-789-2020, 2020
R. Lindenbergh, S. van der Kleij, M. Kuschnerus, S. Vos, and S. de Vries
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1039–1046, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1039-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1039-2019, 2019
L. Truong-Hong, D. F. Laefer, and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1135–1140, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1135-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1135-2019, 2019
B. B. van der Horst, R. C. Lindenbergh, and S. W. J. Puister
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1141–1148, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1141-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1141-2019, 2019
Y. Zang and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1169–1175, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1169-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1169-2019, 2019
Y. Ao, J. Wang, M. Zhou, R. C. Lindenbergh, and M. Y. Yang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 13–20, https://doi.org/10.5194/isprs-archives-XLII-2-W13-13-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-13-2019, 2019
K. Zhou, Y. Chen, I. Smal, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 155–161, https://doi.org/10.5194/isprs-archives-XLII-2-W13-155-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-155-2019, 2019
K. Anders, R. C. Lindenbergh, S. E. Vos, H. Mara, S. de Vries, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 317–324, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, 2019
A. Kumar, K. Anders, L Winiwarter, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 373–380, https://doi.org/10.5194/isprs-annals-IV-2-W5-373-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-373-2019, 2019
M. Soilán, R. Lindenbergh, B. Riveiro, and A. Sánchez-Rodríguez
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 445–452, https://doi.org/10.5194/isprs-annals-IV-2-W5-445-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-445-2019, 2019
Julia Boike, Jan Nitzbon, Katharina Anders, Mikhail Grigoriev, Dmitry Bolshiyanov, Moritz Langer, Stephan Lange, Niko Bornemann, Anne Morgenstern, Peter Schreiber, Christian Wille, Sarah Chadburn, Isabelle Gouttevin, Eleanor Burke, and Lars Kutzbach
Earth Syst. Sci. Data, 11, 261–299, https://doi.org/10.5194/essd-11-261-2019, https://doi.org/10.5194/essd-11-261-2019, 2019
Short summary
Short summary
Long-term observational data are available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, processing, and data quality control. Furthermore, we present a merged dataset of the parameters, which were measured from 1998 onwards.
R. P. A. Bormans, R. C. Lindenbergh, and F. Karimi Nejadasl
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 141–148, https://doi.org/10.5194/isprs-archives-XLII-2-141-2018, https://doi.org/10.5194/isprs-archives-XLII-2-141-2018, 2018
I. Puente, R. Lindenbergh, A. Van Natijne, R. Esposito, and R. Schipper
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 923–929, https://doi.org/10.5194/isprs-archives-XLII-2-923-2018, https://doi.org/10.5194/isprs-archives-XLII-2-923-2018, 2018
B. Riveiro, G. Cubreiro, B. Conde, M. Cabaleiro, R. Lindenbergh, M. Soilán, and J. C. Caamaño
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 969–974, https://doi.org/10.5194/isprs-archives-XLII-2-969-2018, https://doi.org/10.5194/isprs-archives-XLII-2-969-2018, 2018
A. L. van Natijne, R. C. Lindenbergh, and R. F. Hanssen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1137–1144, https://doi.org/10.5194/isprs-archives-XLII-2-1137-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1137-2018, 2018
J. Wang and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1163–1168, https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018, 2018
E. Widyaningrum, R. C. Lindenbergh, B. G. H. Gorte, and K. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1199–1205, https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018, 2018
K. Zhou, B. Gorte, R. Lindenbergh, and E. Widyaningrum
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1229–1235, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, 2018
S. Crommelinck, B. Höfle, M. N. Koeva, M. Y. Yang, and G. Vosselman
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 81–88, https://doi.org/10.5194/isprs-annals-IV-2-81-2018, https://doi.org/10.5194/isprs-annals-IV-2-81-2018, 2018
M. Scaioni, B. Höfle, A. P. Baungarten Kersting, L. Barazzetti, M. Previtali, and D. Wujanz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1503–1510, https://doi.org/10.5194/isprs-archives-XLII-3-1503-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1503-2018, 2018
Y. Shen, R. Lindenbergh, B. Hofland, and R. Kramer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 139–147, https://doi.org/10.5194/isprs-annals-IV-2-W4-139-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-139-2017, 2017
M. Hämmerle, N. Lukač, K.-C. Chen, Zs. Koma, C.-K. Wang, K. Anders, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 59–65, https://doi.org/10.5194/isprs-annals-IV-2-W4-59-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-59-2017, 2017
Sabrina Marx, Katharina Anders, Sofia Antonova, Inga Beck, Julia Boike, Philip Marsh, Moritz Langer, and Bernhard Höfle
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2017-49, https://doi.org/10.5194/esurf-2017-49, 2017
Revised manuscript has not been submitted
Short summary
Short summary
Global climate warming causes permafrost to warm and thaw, and, consequently, to release the carbon into the atmosphere. Terrestrial laser scanning is evaluated and current methods are extended in the context of monitoring subsidence in Arctic permafrost regions. The extracted information is important to gain a deeper understanding of permafrost-related subsidence processes and provides highly accurate ground-truth data which is necessary for further developing area-wide monitoring methods.
Luisa Griesbaum, Sabrina Marx, and Bernhard Höfle
Nat. Hazards Earth Syst. Sci., 17, 1191–1201, https://doi.org/10.5194/nhess-17-1191-2017, https://doi.org/10.5194/nhess-17-1191-2017, 2017
Short summary
Short summary
This study provides a new method for flood documentation based on user-generated flood images. We demonstrate how flood elevation and building inundation depth can be derived from photographs by means of 3-D reconstruction of the scene. With an accuracy of 0.13 m ± 0.10 m, the derived building inundation depth can be used to facilitate damage assessment.
A. Nurunnabi, Y. Sadahiro, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 63–70, https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017, 2017
J. Böhm, M. Bredif, T. Gierlinger, M. Krämer, R. Lindenberg, K. Liu, F. Michel, and B. Sirmacek
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 301–307, https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016, 2016
S. Bechtold and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 161–168, https://doi.org/10.5194/isprs-annals-III-3-161-2016, https://doi.org/10.5194/isprs-annals-III-3-161-2016, 2016
V. H. Phan, R. C. Lindenbergh, and M. Menenti
The Cryosphere Discuss., https://doi.org/10.5194/tcd-8-2425-2014, https://doi.org/10.5194/tcd-8-2425-2014, 2014
Revised manuscript not accepted
J. F. Levinsen, K. Khvorostovsky, F. Ticconi, A. Shepherd, R. Forsberg, L. S. Sørensen, A. Muir, N. Pie, D. Felikson, T. Flament, R. Hurkmans, G. Moholdt, B. Gunter, R. C. Lindenbergh, and M. Kleinherenbrink
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-5433-2013, https://doi.org/10.5194/tcd-7-5433-2013, 2013
Revised manuscript not accepted
V. H. Phan, R. C. Lindenbergh, and M. Menenti
Hydrol. Earth Syst. Sci., 17, 4061–4077, https://doi.org/10.5194/hess-17-4061-2013, https://doi.org/10.5194/hess-17-4061-2013, 2013
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
Useful Links
Useful External Links
Our Contact
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org