scholarly journals The Potential of ICESat-2 to Identify Carbon-Rich Peatlands in Indonesia

2020 ◽  
Vol 12 (24) ◽  
pp. 4175
Author(s):  
Anna Berninger ◽  
Florian Siegert
Keyword(s):  
Surface Topography ◽  
Airborne Lidar ◽  
Accurate Information ◽  
Lidar Data ◽  
Digital Terrain ◽  
Terrain Models ◽  
Tropical Peatlands ◽  
3D Surface Topography ◽  
To Come ◽  
Carbon Store

Peatlands in Indonesia are one of the primary global storages for terrestrial organic carbon. Poor land management, drainage, and recurrent fires lead to the release of huge amounts of carbon dioxide. Accurate information about the extent of the peatlands and its 3D surface topography is crucial for assessing and quantifying this globally relevant carbon store. To identify the most carbon-rich peatlands—dome-shaped ombrogenous peat—by collecting GPS-based terrain data is almost impossible, as these peatlands are often located in remote areas, frequently flooded, and usually covered by dense tropical forest vegetation. The detection by airborne LiDAR or spaceborne remote sensing in Indonesia is costly and laborious. This study investigated the potential of the ICESat-2/ATLAS LiDAR satellite data to identify and map carbon-rich peatlands. The spaceborne ICESat-2 LiDAR data were compared and correlated with highly accurate field validated digital terrain models (DTM) generated from airborne LiDAR as well as the commercial global WorldDEM DTM dataset. Compared to the airborne DTM, the ICESat-2 LiDAR data produced an R2 of 0.89 and an RMSE of 0.83 m. For the comparison with the WorldDEM DTM, the resulting R2 lay at 0.94 and the RMSE at 0.86 m. We model the peat dome surface from individual peat hydrological units by performing ordinary kriging on ICESat-2 DTM-footprint data. These ICESat-2 based peatland models, compared to a WorldDEM DTM and airborne DTM, produced an R2 of 0.78, 0.84, and 0.94 in Kalimantan and an R2 of 0.69, 0.72, and 0.85 in Sumatra. The RMSE ranged from 0.68 m to 2.68 m. These results demonstrate the potential of ICESat-2 in assessing peat surface topography. Since ICESat-2 will collect more data worldwide in the years to come, it can be used to survey and map carbon-rich tropical peatlands globally and free of charge.

scholarly journals Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2292 ◽  
Author(s):  
Wen Liu ◽  
Fumio Yamazaki ◽  
Yoshihisa Maruyama
Keyword(s):  
Image Interpretation ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Digital Terrain ◽  
Terrain Models ◽  
Short Period ◽  
Vertical Displacements ◽  
Good Potential ◽  
Collapsed Buildings ◽  
Airborne Lidar Data

A series of earthquakes hit Kumamoto Prefecture, Japan, continuously over a period of two days in April 2016. The earthquakes caused many landslides and numerous surface ruptures. In this study, two sets of the pre- and post-event airborne Lidar data were applied to detect landslides along the Futagawa fault. First, the horizontal displacements caused by the crustal displacements were removed by a subpixel registration. Then, the vertical displacements were calculated by averaging the vertical differences in 100-m grids. The erosions and depositions in the corrected vertical differences were extracted using the thresholding method. Slope information was applied to remove the vertical differences caused by collapsed buildings. Then, the linked depositions were identified from the erosions according to the aspect information. Finally, the erosion and its linked deposition were identified as a landslide. The results were verified using truth data from field surveys and image interpretation. Both the pair of digital surface models acquired over a short period and the pair of digital terrain models acquired over a 10-year period showed good potential for detecting 70% of landslides.

scholarly journals Optimisation of LiDAR derived terrain models for river flow modelling

2009 ◽  
Vol 13 (8) ◽  
pp. 1453-1466 ◽  
Author(s):  
G. Mandlburger ◽  
C. Hauer ◽  
B. Höfle ◽  
H. Habersack ◽  
N. Pfeifer
Keyword(s):  
River Flow ◽  
Digital Terrain Model ◽  
Expansion Ratio ◽  
Simulation Software ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Random Measurement Error ◽  
Flow Modelling ◽  
Digital Terrain ◽  
Terrain Models

Abstract. Airborne LiDAR (Light Detection And Ranging) combines cost efficiency, high degree of automation, high point density of typically 1–10 points per m2 and height accuracy of better than ±15 cm. For all these reasons LiDAR is particularly suitable for deriving precise Digital Terrain Models (DTM) as geometric basis for hydrodynamic-numerical (HN) simulations. The application of LiDAR for river flow modelling requires a series of preprocessing steps. Terrain points have to be filtered and merged with river bed data, e.g. from echo sounding. Then, a smooth Digital Terrain Model of the Watercourse (DTM-W) needs to be derived, preferably considering the random measurement error during surface interpolation. In a subsequent step, a hydraulic computation mesh has to be constructed. Hydraulic simulation software is often restricted to a limited number of nodes and elements, thus, data reduction and data conditioning of the high resolution LiDAR DTM-W becomes necessary. We will present a DTM thinning approach based on adaptive TIN refinement which allows a very effective compression of the point data (more than 95% in flood plains and up to 90% in steep areas) while preserving the most relevant topographic features (height tolerance ±20 cm). Traditional hydraulic mesh generators focus primarily on physical aspects of the computation grid like aspect ratio, expansion ratio and angle criterion. They often neglect the detailed shape of the topography as provided by LiDAR data. In contrast, our approach considers both the high geometric resolution of the LiDAR data and additional mesh quality parameters. It will be shown that the modelling results (flood extents, flow velocities, etc.) can vary remarkably by the availability of surface details. Thus, the inclusion of such geometric details in the hydraulic computation meshes is gaining importance in river flow modelling.

scholarly journals Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

2015 ◽  
Vol 7 (8) ◽  
pp. 10996-11015 ◽  
Author(s):  
Xiangyun Hu ◽  
Lizhi Ye ◽  
Shiyan Pang ◽  
Jie Shan
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Digital Terrain Models ◽  
Digital Terrain ◽  
Terrain Models ◽  
Fast Extraction ◽  
Airborne Lidar Data

scholarly journals The use of airborne LiDAR data for the analysis of debris flow events in Switzerland

2008 ◽  
Vol 8 (5) ◽  
pp. 1113-1127 ◽  
Author(s):  
C. Scheidl ◽  
D. Rickenmann ◽  
M. Chiari
Keyword(s):  
Debris Flow ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Digital Terrain Models ◽  
Alpine Regions ◽  
Digital Terrain ◽  
Terrain Models ◽  
Digital Elevation ◽  
Airborne Lidar Data ◽  
Volume Estimates

Abstract. A methodology of magnitude estimates for debris flow events is described using airborne LiDAR data. Light Detection And Ranging (LiDAR) is a widely used technology to generate digital elevation information. LiDAR data in alpine regions can be obtained by several commercial companies where the automated filtering process is proprietary and varies from companies to companies. This study describes the analysis of geomorphologic changes using digital terrain models derived from commercial LiDAR data. The estimation of the deposition volumes is based on two digital terrain models covering the same area but differing in their time of survey. In this study two surveyed deposition areas of debris flows, located in the canton of Berne, Switzerland, were chosen as test cases. We discuss different grid interpolating techniques, other preliminary work and the accuracy of the used LiDAR data and volume estimates.

scholarly journals Optimisation of LiDAR derived terrain models for river flow modelling

2008 ◽  
Vol 5 (6) ◽  
pp. 3605-3638 ◽  
Author(s):  
G. Mandlburger ◽  
C. Hauer ◽  
B. Höfle ◽  
H. Habersack ◽  
N. Pfeifer
Keyword(s):  
River Flow ◽  
Digital Terrain Model ◽  
Expansion Ratio ◽  
Simulation Software ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Random Measurement Error ◽  
Flow Modelling ◽  
Digital Terrain ◽  
Terrain Models

Abstract. Airborne LiDAR (Light Detection And Ranging) combines cost efficiency, high degree of automation, high point density of typically 1–10 points per m2 and height accuracy of better than ±15 cm. For all these reasons LiDAR is particularly suitable for deriving precise Digital Terrain Models (DTM) as geometric basis for hydrodynamic-numerical (HN) simulations. The application of LiDAR for river flow modelling requires a series of preprocessing steps. Terrain points have to be filtered and merged with river bed data, e.g. from echo sounding. Then, a smooth Digital Terrain Model of the Watercourse (DTM-W) needs to be derived, preferably considering the random measurement error during surface interpolation. In a subsequent step, a hydraulic computation mesh has to be constructed. Hydraulic simulation software is often restricted to a limited number of nodes and elements, thus, data reduction and data conditioning of the high resolution LiDAR DTM-W becomes necessary. We will present a DTM thinning approach based on adaptive TIN refinement which allows a very effective compression of the point data (more than 95% in flood plains and up to 90% in steep areas) while preserving the most relevant topographic features (height tolerance ±20 cm). Traditional hydraulic mesh generators focus primarily on physical aspects of the computation grid like aspect ratio, expansion ratio and angle criterion. They often neglect the detailed shape of the topography as provided by LiDAR data. In contrast, our approach considers both the high geometric resolution of the LiDAR data and additional mesh quality parameters. It will be shown that the modelling results (flood extents, flow velocities, etc.) can vary remarkably by the availability of surface details. Thus, the inclusion of such geometric details in the hydraulic computation meshes will gain importance for river flow modelling in the future.

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