Landslide data mosaicking based on an airborne laser point cloud and multi-beam sonar images

Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have predominantly focused on landslides that occur on land.To this end,we aim to investigate ashore and underwater landslide data synchronously.This study proposes an optimized mosaicking method for ashore and underwater landslide data.This method fuses an airborne laser point cloud with multi-beam depth sounder images.Owing to their relatively high efficiency and large coverage area,airborne laser measurement systems are suitable for emergency investigations of landslides.Based on the airborne laser point cloud,the traversal of the point with the lowest elevation value in the point set can be used to perform rapid extraction of the crude channel boundaries.Further meticulous extraction of the channel boundaries is then implemented using the probability mean value optimization method.In addition,synthesis of the integrated ashore and underwater landslide data angle is realized using the spatial guide line between the channel boundaries and the underwater multibeam sonar images.A landslide located on the right bank of the middle reaches of the Yalong River is selected as a case study to demonstrate that the proposed method has higher precision thantraditional methods.The experimental results show that the mosaicking method in this study can meet the basic needs of landslide modeling and provide a basis for qualitative and quantitative analysis and stability prediction of landslides.

Improving the wind and temperature measurements of an airborne meteorological measuring system

The Aircraft Integrated Meteorological Measuring System 20 Hz(AIMMS-20) has been used by the Hong Kong Observatory(HKO),China in data collection for tropical cyclone situations over the South China Sea and windshear and turbulence measurement at the Hong Kong International Airport(HKIA).This paper discusses possible methods for further enhancing the quality of the wind and temperature measurements from the system.For wind measurement,the enhancement methods include:error modelling of the accelerometer(e.g.,bias offset and cross-axis rate sensitivity),global positioning system(GPS) phase lag consideration,better representation of the inertial measurement unit(IMU) velocity based on the GPS velocities and considering their location differences,consideration of the slower update of GPS velocity,and wing flexure.For temperature measurement,the methods include the consideration of the temperature sensor response and the sensor housing response.The results of typical flights using AIMMS-20 show that the accuracy of the wind and temperature data could be improved by 20%-30%.Though the discussion in the present paper is related mainly to a specific meteorological measuring system on a particular aircraft,the techniques so employed should be a useful reference for similar systems installed on other aircraft.

Evaluation of the relationships and uncertainties of airborne and ground-based sea ice surface temperature measurements against remotely sensed temperature records

Sea ice surface temperature(IST)is an important indicator of environmental changes in the Arctic Ocean.In this study,the relative performance of four mainstream IST records,i.e.airborne IST,infrared radiometer measured IST(IR IST),longwave radiation derived IST(LWR IST),and snow and ice mass balance array buoy derived IST(Buoy IST),were evaluated against the MODIS IST product.Bias,standard deviation(STD),and root mean square error(RMSE)were used to evaluate the data quality.Results revealed that airborne IST had the best accuracy,which was 0.21 K colder than MODIS IST,with STD of 1.46 K and RMSE of 1.47 K.Ground-based ISTs were biased with each other but all warmer than the MODIS IST.The IR IST had the best overall accuracy(bias=0.55 K;STD=1.52 K;RMSE=1.61 K),while the LWR IST was the noisiest measurement with the largest outlier data percent.Besides,co-located IR and LWR ISTs were more consistent than any type of evaluated IST against MODIS IST(correlation coefficient=0.99).Airborne and IR ISTs are thus the premier choice for monitoring the rapidly changing Arctic sea ice,together with satellite observations.