scholarly journals Analysis Using High-Precision Airborne LiDAR Data to Survey Potential Collapse Geological Hazards

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jinxing She ◽  
Awei Mabi ◽  
Zhongming Liu ◽  
Mingqiang Sheng ◽  
Xiujun Dong ◽  
...  
Keyword(s):  
High Altitude ◽  
High Precision ◽  
Early Identification ◽  
Comparative Research ◽  
Airborne Lidar ◽  
Cloud Data ◽  
Digital Terrain ◽  
Richter Scale ◽  
Significant Damage ◽  
Airborne Lidar Data

On August 8, 2017, an earthquake of magnitude 7.0 on the Richter scale occurred in Jiuzhaigou, Sichuan, causing significant damage to both life and property. Traditional geological hazard investigation is difficult in Jiuzhaigou because of the high altitude, the high-altitude canyons, and the vegetation-covered seismic areas. This study explores the technical advantages of using airborne LiDAR technology to penetrate vegetation and gather information directly from the surface, rapidly acquiring airborne 3D point cloud data in difficult areas. Through the preprocessing of data, the high-precision digital terrain and landform results were obtained. Comparative research found that the DEM obtained by high-precision airborne LiDAR technology has significant advantages in terms of the accuracy, details, and microgeomorphology of the data collected. The results can be directly used in the early identification of disasters, such as during the initial collapse or for disaster management. Studies have shown that airborne LiDAR has the technical advantage of penetrating vegetation to the surface and can, therefore, be used to guide the early identification and management of geological disasters in similar areas in the future.

scholarly journals 3D-Modelling of Charlemagne’s Summit Canal (Southern Germany)—Merging Remote Sensing and Geoarchaeological Subsurface Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1111 ◽  
Author(s):  
Johannes Schmidt ◽  
Johannes Rabiger-Völlmer ◽  
Lukas Werther ◽  
Ulrike Werban ◽  
Peter Dietrich ◽  
...  
Keyword(s):  
Cross Sections ◽  
3D Model ◽  
Digital Terrain Model ◽  
Airborne Lidar ◽  
Engineering Construction ◽  
Terrain Model ◽  
Digital Terrain ◽  
Danube Catchment ◽  
The Difference ◽  
Airborne Lidar Data

The Early Medieval Fossa Carolina is the first hydro-engineering construction that bridges the Central European Watershed. The canal was built in 792/793 AD on order of Charlemagne and should connect the drainage systems of the Rhine-Main catchment and the Danube catchment. In this study, we show for the first time, the integration of Airborne LiDAR (Light Detection and Ranging) and geoarchaeological subsurface datasets with the aim to create a 3D-model of Charlemagne’s summit canal. We used a purged Digital Terrain Model that reflects the pre-modern topography. The geometries of buried canal cross-sections are derived from three archaeological excavations and four high-resolution direct push sensing transects. By means of extensive core data, we interpolate the trench bottom and adjacent edges along the entire canal course. As a result, we are able to create a 3D-model that reflects the maximum construction depth of the Carolingian canal and calculate an excavation volume of approx. 297,000 m3. Additionally, we compute the volume of the present dam remnants by Airborne LiDAR data. Surprisingly, the volume of the dam remnants reveals only 120,000 m3 and is much smaller than the computed Carolingian excavation volume. The difference reflects the erosion and anthropogenic overprint since the 8th century AD.

scholarly journals ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL TERRAIN MODEL (DTM) WITH DIFFERENT SLOPE AND CANOPY COVER IN TROPICAL FOREST REGION

2015 ◽  
Vol II-2/W2 ◽  
pp. 183-189 ◽  
Author(s):  
M. R. M. Salleh ◽  
Z. Ismail ◽  
M. Z. A. Rahman
Keyword(s):  
Tropical Forest ◽  
Digital Terrain Model ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Terrain Slope ◽  
Airborne Lidar Data

Airborne Light Detection and Ranging (LiDAR) technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM). High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover) and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN) algorithm technique in producing ground points. Next, the ground control points (GCPs) used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870) with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924) obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

scholarly journals UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy)

Geosciences ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 248 ◽  
Author(s):  
Roberta Pellicani ◽  
Ilenia Argentiero ◽  
Paola Manzari ◽  
Giuseppe Spilotro ◽  
Cosimo Marzo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Southern Italy ◽  
Airborne Lidar ◽  
Slope Instability ◽  
Digital Terrain ◽  
Kinematic Evolution ◽  
Terrain Models ◽  
Landslide Body ◽  
Remote Sensing Systems ◽  
Airborne Lidar Data

Airborne remote sensing systems are increasingly used in engineering geology and geomorphology for studying and monitoring natural hazardous scenarios and events. In this study, we used two remote sensing monitoring techniques, i.e., light detection and ranging (LiDAR) and unmanned aerial vehicles (UAV) to analyze the kinematic evolution of the Montescaglioso landslide (Basilicata, Southern Italy), a large rain-triggered landslide that occurred in December 2013. By comparing pre- and post-event LiDAR and UAV DEMs and UAV orthomosaics, we delineated landslide morphological features and measured horizontal displacements and elevation change differences within landslide body. Analysis of two subsequent post-events digital terrain models (DTMs) also allowed the evaluation of the evolutionary behavior of the slope instability, highlighting no signs of reactivation. The UAV-derived digital surface models (DSMs) were found consistent with the LiDAR-DTMs, but their use was in addition highlighted as highly effective to support geomorphic interpretations and complement LiDAR and field-based data acquisitions. This study shows the effectiveness of combining the two UAV-LiDAR methodologies to evaluate geomorphological features indicative of the failure mechanism and to interpret the evolutionary behavior of the instability process

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 Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3961
Author(s):  
Zhanyuan Chang ◽  
Huiling Yu ◽  
Yizhuo Zhang ◽  
Keqi Wang
Keyword(s):  
Object Classification ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Residual Network ◽  
Cloud Data ◽  
Ground Object ◽  
Fusion Scheme ◽  
Airborne Lidar Data

Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources. Single remote-sensing modality is not sufficient for the accurate and satisfactory extraction and classification of ground objects. Hyperspectral imaging has been widely used in the classification of ground objects because of its high resolution, multiple bands, and abundant spatial and spectral information. Moreover, the airborne light detection and ranging (LiDAR) point-cloud data contains unique high-precision three-dimensional (3D) spatial information, which can enrich ground object classifiers with height features that hyperspectral images do not have. Therefore, the fusion of hyperspectral image data with airborne LiDAR point-cloud data is an effective approach for ground object classification. In this paper, the effectiveness of such a fusion scheme is investigated and confirmed on an observation area in the middle parts of the Heihe River in China. By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification. Firstly, we used the minimum noise fraction transform to reduce the dimensionality of hyperspectral CASI images. Then, spatio-spectral and textural features of these images were extracted based on the normalized vegetation index and the gray-level co-occurrence matrices. Further, canopy height features were extracted from airborne LiDAR data. Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification. The experimental results showed that the overall classification accuracy was based on the proposed hierarchical-fusion multiscale dilated residual network (M-DRN), which reached an accuracy of 97.89%. This result was found to be 10.13% and 5.68% higher than those of the convolutional neural network (CNN) and the dilated residual network (DRN), respectively. Spatio-spectral and textural features of hyperspectral CASI images can complement the canopy height features of airborne LiDAR data. These complementary features can provide richer and more accurate information than individual features for ground object classification and can thus outperform features based on a single remote-sensing modality.

scholarly journals A PHYSICAL INVERSION METHOD OF CANOPY FPAR FROM AIRBORNE LIDAR DATA AND GROUND MEASUREMENTS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 553-557
Author(s):  
X. Yang ◽  
X. Xi ◽  
C. Wang ◽  
J. Shi ◽  
Y. Huang
Keyword(s):  
Structural Information ◽  
Three Dimensional ◽  
Terrestrial Ecosystem ◽  
Field Measurements ◽  
Airborne Lidar ◽  
Inversion Method ◽  
Lidar Data ◽  
Growth Monitoring ◽  
Cloud Data ◽  
Airborne Lidar Data

Abstract. Fraction of absorbed Photosynthetically Active Radiation (FPAR) is one of the pivotal parameters in terrestrial ecosystem modelling and crop growth monitoring. Airborne LiDAR is an advanced active remote sensing technology which can acquire fine three-dimensional canopy structural information quickly and accurately. Although some previous studies have shown that LiDAR-derived metrics had strong relationships with canopy FPARs, these estimation models without physical meaning are hard to be extended to various vegetation canopies and different growth periods. This study proposed a physical FPAR inversion method based on airborne LiDAR data and field measurements. The method considered direct and diffuse radiations separately based on the SAIL model and energy budget balance principle. The canopy FPAR was inversed from the structural information provided by LiDAR point cloud data and the spectral information provided by ground measurements. The estimated FPAR was validated with the field-measured FPAR over 39 maize plots. Results showed that the proposed method had a good performance in estimating the total FPAR of maize canopy (R2 = 0.76, RMSE = 0.062, n = 39). This study provides the potential to estimate the total, direct, and diffuse FPARs of vegetation canopy from airborne LiDAR data.

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