Lidar remote sensing

Introduction

Lidar is a remote sensing technology which uses electromagnetic energy in the optical range to detect an object that measures distance by illuminating a target with a laser and analyzing the reflected light Lidar is popularly used as a technology to make high-resolution maps, in Geomatics, Archaeology, Geography, Geology, Geomorphology, Seismology, Forestry, Remote sensing, Atmospheric physics, Airborne laser swath mapping , Laser altimetry, and Contour mapping. [1-6]

Applications: Agriculture, Autonomous vehicles Biology and conservation, Geology and soil science, Atmospheric remote sensing ,meteorology, Military, Mining, Physics and astronomy, Robotics Spaceflight, Surveying, Transport, Wind farm optimization ,Solar photovoltaic deployment optimization. [7-20]

Discussion

• LiDAR Data for Characterizing Linear and Planar Geomorphic Markers in Tectonic Geomorphology : The communications between tectonic movement, erosion and sedimentation can alter linear geomorphic features, cases of linear geomorphic markers -Offset channels connected with strike-slip faults it can be utilized to focus the rate and nature of tectonic movement. Planar geomorphic features, for example, fluvial terraces, alluvial fans, and marine terraces utilized as geomorphic markers as a part of tectonic disfigurement studies. [21-25]

Airborne and terrestrial LiDAR for portraying linear and planar geomorphic markers, incorporating dynamic faults in urban and forest environments and deformation caused by seismic tremors.

• Automatic extraction of 3D objects from LIDAR data: LIDAR information has interesting properties for programmed extraction of 3-D objects. The most critical and invariant property of a 3-D protest in LIDAR information is 3-D. As it were, the very accessibility of Z distinguishes objects better than the 2-D image view. This property is utilized to distinguish, extract, and label 3-D objects automatically. [26-30]

• Estimation of Above-Ground Forest Biomass using Lidar: Airborne scanning LiDAR is a system for productive and precise biomass mapping because of its ability for direct estimation of the three-dimensional structure of vegetation. The estimation of above ground biomass in forests is critical for carbon cycle modeling and climate change alleviation programs. Little footprint lidar gives exact biomass estimates, however its application in tropical forests has been constrained, Hyper spectral data record canopy spectral information that is possibly identified with forest biomass. [31-39]

• Light Detection and Ranging using LiDAR: Recognized as an effective and Economic strategy for collecting different sorts of roadway resource data. LIDAR Light Detection and Ranging is a remote sensing innovation that gathers 3-dimensional point clouds of the Earth's surface and is utilized for an extensive variety of uses including high- resolution topographic mapping and 3-dimensional surface modeling and also infrastructure and biomass studies. [40-45]

• Development and Flight Test of an Avionics Lidar for Helicopter: The sensors incorporate a 3-Dimensional Imaging Flash Lidar, a Doppler Lidar, and a Laser Altimeter. The Flash Lidar can produce terrain elevation maps that demonstrate dangerous highlights, for example, rocks, craters, and slopes. The Doppler Lidar gives exceptionally precise vector speed and elevation information taking into account accuracy route to landing sites. The Laser Altimeter gives extremely precise ground relative elevation estimations from more than 20 km. The lidars worked in conjunction with other ALHAT landing subsystems (Guidance Navigation & Control, peril recognition/safe site determination figure component, and so forth.) as a coordinated framework [46-48]

• Automatic Segmentation of Lidar Data: Programmed extraction of building rooftops from remote sensing information is imperative for some applications, including 3D city modelling. [49-55]

Conclusion

Lidar remote sensing has become available as a research tool and precise apparatus for measuring topography, vegetation height, and in addition more intricate characteristics of canopy structure and function. Lidar has many applications in the scientific, engineering, and military fields. Lidar sensors have been deployed at fixed terrestrial stations, in portable surface and subsurface vehicles, lighter-than-air craft’s, settled and rotating wing flying machine, satellites, interplanetary probes, and planetary Landers and meanderers.

References

1. Dong P . Applications of Light Detection and Ranging (LiDAR) in Geosciences. J Geol Geosci. 2012; 1: e102.
2. Dong P. LiDAR Data for Characterizing Linear and Planar Geomorphic Markers in Tectonic G eomorphology. J Geophys Remote Sensing.2015; 4:136.
3. Bellakaout A, Cherkaoui Omari M, Ettarid M, Touzani A .Automatic Extraction of 3D Objects from LiDAR Data. J Archit Eng Tech.2014; 3:123.
4. Cortés L, Hernández J, Valencia D, Corvalán P. Estimation of Above-Ground Forest Biomass Using Landsat ETM+, Aster GDEM and Lidar. Forest Res.2014; 3:117.
5. Sabatini R, Richardson MA, Roviaro E. Development and Flight Test of an Avionics Lidar for Helicopter and UAV Low-Level Flight. J Aeronaut Aerospace Eng.2013; 2:114.
6. Bellakaout A, Omari Mohammed C, Mohamed E, Abderrahmane T. Automatic Segmentation of Lidar Data. J Archit Eng Tech.2014; 3:124.
7. Qin X, Yin Z. Earthquake and Local Rainfall Triggered Giant Landslides in the Unconsolidated Sediment Distribution Region along the Upper Yellow River-Remote Sense Analysis of Geological Disasters. J Geophys Remote Sensing.2012; 1:e105.
8. Salimi P, Motlagh AT. Mapping of the Bedrock Topography Using Gravity Data: A Case Study in the South of Hormozgan Province, Iran. J Geophys Remote Sensing.2012; 1:105.
9. Tholkapiyan M, Shanmugam P, Chauhan P, Suresh M .Derivation of Calibration Coefficients for OCM-2 Sensor for Coastal Waters. J Geophys Remote Sensing.2012; 1:106.
10. Potter C, Li S, Crabtree R. Changes in Alaskan Tundra Ecosystems Estimated from MODIS Greenness Trends, 2000 to 2010. J Geophys Remote Sensing.2013; 2:107.
11. Pan S, Li G, Yang Q, Ouyang Z, Lockaby G, et al. Monitoring Land-Use and Land-Cover Change in the Eastern Gulf Coastal Plain 3 using Multitemporal Landsat imagery. J Geophys Remote Sensing. 2013; 2:108.
12. Sheela AM, Letha J, Joseph S, Ramachandran KK, Justus J .Assessment of Pollution Status of a Coastal Lake System using Satellite Imagery. J Geophys Remote Sensing.2013; 2:110.
13. Kiryukhin AV, Kiryukhin VA, Manuhin YF. Hydrogeology of Volcanic Regions (in Russian). Nauka, Saint- Petersburg, 2010. 395 p. A book review by Zaltsberg E . J Geophys Remote Sensing.2013; 2:111.
14. Guo-Qiang X, Wei-Ying C, Nan-Nan Z, Hai L, Hua-Sen Z. Understanding of Grounded-Wire TEM Sounding with Near-Source Configuration. J Geophys Remote Sensing.2013; 2:113.
15. Shi L, Guo L, Chen S, Xu W .Dipole Correlation Method for Determination of Magnetization Direction under the Influence of Remanent Magnetization. J Geophys Remote Sensing.2013; S4:001.
16. Loudon JA. Time of Flight Survey by Artificially Generated Neutrinos: A Novel Complementary Approach in Remote Sensing Geophysics. J Geophys Remote Sensing.2014; 3:117.
17. Iqbal M, Sajjad H . Watershed Prioritization using Morphometric and Land Use/Land Cover Parameters of Dudhganga Catchment Kashmir Valley India using Spatial Technology. J Geophys Remote Sensing.2014; 3:115.
18. Mani ND, Rama Krishnan N . Remote Sensing and GIS for Assessing the Impact of Changes on Land Use/Land Cover in Dindigul District, Tamil Nadu, India. J Geophys Remote Sensing.2014; 3:114.
19. Mukherjee S. Extra-terrestrial Geophysics to Understand Climate Change and Open Access Journal: Journal of Geophysics and Remote Sensing. J Geophys Remote Sensing.2014; 3:e109.
20. Chun-Ming H, Fu-Yuan W, Wen-JiaoX, Guo-Chun Z, Song-Jian A, et al. The Paleoproterozoic Chibaisong Mafic- Ultramafic Intrusion and Cu-Ni Deposit, North China Craton: SHRIMP Zircon U-Pband Re-Os Geochronology and Geodynamic Implications. J Geophys Remote Sensing.2014; 3:116.
21. Asil SRH, Ardejan FD, Moakhar MO; An Automatic Method for Anomalies Sources Depth Determination from Magnetic Data (Case Study: Salafchegan Area, Qom, Iran). J Geophys Remote Sensing.2014; 3:119.
22. Saleh SAH, Hasan G. Estimation of PM10 Concentration using Ground Measurements and Landsat 8 OLI Satellite Image. J Geophys Remote Sens.2014; 3:120.
23. Sharma MS. Estimating the Mineral Exploration Inside Earth Crusts. J Geophys Remote Sens.2014; 3:121.
24. Boori M S, V ožen ílek V . Remote Sensing and L and Us e/L and Cover T rajectories . J Geophys Remote Sensing.2014; 3:123.
25. Mukherjee S .Extra Terrestrial Remote Sensing and Geophysical Applications to Understand Kedarnath Cloudburst in Uttarakhand, India. J Geophys Remote Sensing.2014; 3:124.
26. Xu Y, Hao T, Duan Q, Zhang J. Deep Concealed Coastal Fault of Dagang Area, North China: Geophysical Characteristics from Potential Field Data and its Geological Implications. J Geophys Remote Sensing .2014; 3:127.
27. Kuhle M. Geomorphologic Case Studies on the Current and Former Glacier Dynamics in High Asia. J Geophys Remote Sensing.2014; 3:128.
28. Shepard T, Abraham J, Schwalbach D, Kane S, Siglin D, et al. Velocity and Density Effect on Impact Force during Water Entry of Sphere. J Geophys Remote Sensing.2014; 3:129.
29. Mesta PN, Setturu B, Subash Chandran MD, Rajan KS, Ramachandra TV. Inventorying, Mapping and Monitoring of Mangroves towards Sustainable Management of West Coast, India. J Geophys Remote Sensing.2014 ; 3:130.
30. Shirodkar PV, Banerjee R, Xiao YK . A Revisit to Vityaz Transform Fault Area, Central Indian Ridge: Isotopic Evidence of Probable Hydrothermal Activity. J Geophys Remote Sensing.2014; 3:125.
31. Choudhary K, Boori MS and Novacek P. A Multi-Criteria Approach: For Empowerment to Enhancing Community Sustainable Development. J Geophys Remote Sensing.2014; 3:e110.
32. Akbari A, Meshinchi-Asl M, Riyahi MA. An Improved Hyperbolic Summation Imaging Algorithm for Detection of the Subsurface Targets. J Geophys Remote Sensing.2014; 3:132.
33. Lissner J. Challenges to Geophysical Ecosystems and Proposals for Improvement. J Geophys RemoteSensing.2014; 3:133.
34. Alaminiokuma GI, Emudianughe JE. Delineating the Proper Shooting Medium for Seismic Reflection Survey inthe Greater Ughelli Depobelt,Niger Delta. J Geophys Remote Sensing.2014; 3:134.
35. Anno S, Imaoka K, Tadono T, Igarashi T, Sivaganesh S, et al. Assessing the Temporal and Spatial Dynamics of the Dengue Epidemic in Northern Sri Lanka using Remote Sensing Data, GIS and Statistical Analysis. J Geophys Remote Sensing.2014; 3:135.
36. Boori MS, Vozenílek V, Balzter H, Choudhary K. Land Surface Temperature with Land Cover Classes in ASTER and Landsat Data. J Geophys Remote Sensing.2015; 4:138.
37. Day CA. Satellite Data Utilization in Hydrological Research. J Geol Geosci.2013; 2:e108.
38. Boori MS, Ferraro RR. Microwave Polarization and Gradient Ratio (MPGR) for Global Land Surface Phenology. J Geol Geosci.2013; 2:114.
39. Huang X, Pan T, Ruan H, Fu H, Yang G. Parallel Buffer Generation Algorithm for GIS. J Geol Geosci.2013; 2:115.
40. Falloon P, Fereday D, Stringer N, Williams K, Gornall J, et al. Assessing Skill for Impacts in Seasonal to Decadal Climate Forecasts. J Geol Geosci.2013; 2:e111.
41. Yimin Z, Yue-xiang G, Jing-ying C, Long-mian W, Fei Y, et al. Thinking of Wetland Reconstruction and Ecological Remediation for Shallow Lakes. J Geol Geosci.2013; 2:e112.
42. Elhabab AAA, Adsani I. Geochemical and Mineralogical Characters of the Coastal Plain Sediments of the Arabian Gulf, Kuwait. J Geol Geosci.2013; 3:137.
43. Johnson TR, Long TC, Barnard WF. A Pilot Study to Identify Factors Affecting UV-B Radiation Exposure in Selected Microenvironments. J Geol Geosci.2013; 3:136.
44. Nevtipilova V, Pastwa J, Boori MS, Vozenilek V. Testing Artificial Neural Network (ANN) for Spatial Interpolation. J Geol Geosci.2013; 3: 145.
45. Kinthada NR, Gurram MK, Eadara A, Velagala VR. Land Use/Land Cover and NDVI Analysis for Monitoring the Health of Micro-watersheds of Sarada River Basin, Visakhapatnam District, India. J Geol Geosci.2014; 3: 146.
46. Boor i MS, V ožení lek V . Rem ot e Sens ing and G I S for Soc io -Hydrological Vulnerability. J Geol Geosci.2014; 3:e115.
47. Boori MS, Ferraro RR, VoženÃlek V. NASA EOS Aqua Satellite AMSR-E Data for Snow Variation. J Geol Geosci.2014; 3:e116.
48. Krishna MK, Song G, Kantham NL .Bio-physical Changes in the Eastern Arabian Sea due to Ocean-Atmospheric Teleconnections. J Geol Geosci.2014; 3:165.
49. Kilias AD, Vamvaka A, Falalakis G, Sfeikos A, Papadimitriou E, et al. The Mesohellenic Trough and the Paleogene Thrace Basin on the Rhodope Massif, their Structural Evolution and Geotectonic Significance in the Hellenides. J Geol Geosci.2015; 4:198.
50. Adeniran AO. Principal Component and Cluster Analyses of Palynodebris from Aj-X Well, Offshore Dahomey Basin and their Environmental Significance. J Geol Geosci.2015; 4:189.
51. Boori MS, Choudhary K. Remote Sensing for Vegetation and Climate Change. J Geophys Remote Sensing.2015 4:e111.
52. Elhoucine Essefia B, Najoua Gharsallia B, Sabrine Kalabi AB, Mohamed AB, Yaicha BC.Spectral Analysis of a Core from the Sebkha of Sidi Mansour, Southern Tunisia: The Holocene Cyclostratigraphy. J Geophys Remote Sensing.2015; 4:141.
53. Abdullahi MG, Garba I. Effect of Rainfall on Groundwater Level Fluctuation in Terengganu, Malaysia. J Geophys Remote Sensing.2015; 4:142.
54. Uko ED, Emudianughe JE . AVO Modelling of the South-East Niger Delta, Nigeria. J Geophys Remote Sensing.2014; 3:131.
55. Palaniyandi M, Anand PH, Maniyosai R . Climate, Landscape and the Environments of Visceral Leishmaniasis Transmission in India, Using Remote Sensing and GIS. J Geophys Remote Sensing.2014; 3:122.