Lake Level Reconstructed From DEM-Based Virtual Station: Comparison of Multisource DEMs With Laser Altimetry and UAV-LiDAR Measurements

2021 ◽  
pp. 1-5
Author(s):  
Pengfei Zhan ◽  
Chunqiao Song ◽  
Shuangxiao Luo ◽  
Kai Liu ◽  
Linghong Ke ◽  
...  
Keyword(s):  
Lake Level ◽  
Laser Altimetry ◽  
Virtual Station ◽  
Lidar Measurements

Large-eddy simulations and lidar measurements of vortex-pair breakup in aircraft wakes

AIAA Journal ◽  
1998 ◽  
Vol 36 ◽  
pp. 1439-1445 ◽  
Author(s):  
D. C. Lewellen ◽  
W. S. Lewellen ◽  
L. R. Poole ◽  
C. A. Hostetler ◽  
R. J. DeCoursey ◽  
...  
Keyword(s):  
Vortex Pair ◽  
Large Eddy Simulations ◽  
Large Eddy ◽  
Lidar Measurements

SOURCES OF CARBONATE PRECIPITATION IN MONO LAKE SEDIMENTS: δ18O AND CARBONATE CLUMPED ISOTOPES AS LAKE LEVEL PROXIES

2017 ◽  
Author(s):  
Miquela Ingalls ◽  
◽  
Sophie Westacott ◽  
Makayla Betts ◽  
Jana Meixnerova ◽  
...  
Keyword(s):  
Lake Sediments ◽  
Lake Level ◽  
Mono Lake ◽  
Carbonate Precipitation ◽  
Clumped Isotopes

scholarly journals Improving InSAR elevation models in Antarctica using laser altimetry, accounting for ice motion, orbital errors and atmospheric delays

2015 ◽  
Vol 162 ◽  
pp. 112-118 ◽  
Author(s):  
Yu Zhou ◽  
Chunxia Zhou ◽  
Fanghui Deng ◽  
Dongchen E ◽  
Haiyan Liu ◽  
...  
Keyword(s):  
Laser Altimetry ◽  
Atmospheric Delays

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

Rock varnish record of the African Humid Period in the Lake Turkana basin of East Africa

The Holocene ◽  
2021 ◽  
pp. 095968362110116
Author(s):  
Tanzhuo Liu ◽  
Christopher J Lepre ◽  
Sidney R Hemming ◽  
Wallace S Broecker
Keyword(s):  
East Africa ◽  
Lake Level ◽  
Drainage System ◽  
Early Holocene ◽  
Lake Turkana ◽  
Moisture Condition ◽  
Turkana Basin ◽  
Rock Varnish ◽  
Geomorphic Features ◽  
Humid Period

Rock varnish is a manganiferous dark coating accreted on subaerially exposed rocks in drylands. It often contains a layered microstratigraphy that records past wetness variations. Varnish samples from latest Pleistocene and Holocene geomorphic features in the Lake Turkana basin, East Africa display a regionally replicable microstratigraphy record of Holocene millennial-scale wetness variability and a broad interval of wetter conditions during the African Humid Period (AHP). Three major wet pulses in the varnish record occurred during the generally wet interval of the early Holocene (11.5–8.5 ka) when the lake attained its maximum high stand (MHS) at 455–460 m. A >23 m drop from the MHS occurred between 8.5 and 8 ka. Subsequently two additional wet pulses occurred during the early to middle Holocene (8–5 ka) when the lake occupied its secondary high stand at 445 m. Collectively, these five wet phases represent an extended wet interval coincident with the AHP in the region. One moderate wet phase occurred during the subsequent climatic transition from the humid to arid regime (5–4.3 ka) after the lake level dropped rapidly from 445 m to <405 m. Five minor wet phases took place during the overall arid period of the late Holocene (4.3–0 ka) when the lake level oscillated below 405 m. These findings indicate that the AHP terminated rapidly around 5 ka in the Turkana basin in terms of lake level drop, but the regional shift in relative humidity from the AHP mode to its present-day condition lagged for about 700 years until 4.3 ka, hinting at a gradual phasing out in terms of moisture condition. These findings further suggest that Lake Turkana overflowed intermittently into the Nile drainage system through its topographic sill at 455–460 m during the early Holocene and has become a closed-basin lake thereafter for the past 8 ky.

scholarly journals Deriving Tree Size Distributions of Tropical Forests from Lidar

2021 ◽  
Vol 13 (1) ◽  
pp. 131
Author(s):  
Franziska Taubert ◽  
Rico Fischer ◽  
Nikolai Knapp ◽  
Andreas Huth
Keyword(s):  
Size Distribution ◽  
Spatial Scales ◽  
Basal Area ◽  
Tree Height ◽  
Tree Size ◽  
Diameter Distribution ◽  
Forest Inventories ◽  
Allometric Relations ◽  
Lidar Measurements ◽  
Derived Data

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

scholarly journals Combined Block Adjustment for Optical Satellite Stereo Imagery Assisted by Spaceborne SAR and Laser Altimetry Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3062
Author(s):  
Guo Zhang ◽  
Boyang Jiang ◽  
Taoyang Wang ◽  
Yuanxin Ye ◽  
Xin Li
Keyword(s):  
Large Scale ◽  
Satellite Image ◽  
Field Measurements ◽  
High Elevation ◽  
Global Scale ◽  
Stereo Image ◽  
Image Process ◽  
Control Points ◽  
Altimetry Data ◽  
Laser Altimetry

To ensure the accuracy of large-scale optical stereo image bundle block adjustment, it is necessary to provide well-distributed ground control points (GCPs) with high accuracy. However, it is difficult to acquire control points through field measurements outside the country. Considering the high planimetric accuracy of spaceborne synthetic aperture radar (SAR) images and the high elevation accuracy of satellite-based laser altimetry data, this paper proposes an adjustment method that combines both as control sources, which can be independent from GCPs. Firstly, the SAR digital orthophoto map (DOM)-based planar control points (PCPs) acquisition is realized by multimodal matching, then the laser altimetry data are filtered to obtain laser altimetry points (LAPs), and finally the optical stereo images’ combined adjustment is conducted. The experimental results of Ziyuan-3 (ZY-3) images prove that this method can achieve an accuracy of 7 m in plane and 3 m in elevation after adjustment without relying on GCPs, which lays the technical foundation for a global-scale satellite image process.

Sign in / Sign up

Export Citation Format

Share Document