Spaceborne LiDAR Surveying and Mapping

Laser point cloud data have the characteristics of high elevation accuracy, fast processing efficiency, strong three-dimensional (3D) vision, and wide application fields. It will be one of the core datasets of the new generation national global topographic database. The rapid advancement of spaceborne laser earth observation technology allows the collection of global 3D point cloud data, which has brought a new breakthrough in the field of satellite-based earth observation, and its significant advantages of all-day time, high accuracy and high efficiency will lead the future development of space precise mapping technology. This chapter firstly introduces the principle and development status of satellite-based LiDAR technology, then presents the basic technical framework of satellite-based LiDAR 3D mapping, and analyzes the data processing methods of spaceborne photon point clouds, and finally, focuses on the application research in various fields including precise geolocation of combined with satellite images, fusion of multi-source topographic information, polar mapping, 3D objects reconstruction, and shallow sea topographic mapping, etc

Analytical Modeling and Performance Assessment of Micropulse Photon-counting Lidar System

The melting of polar ice sheets and evidence of global warming continue to remain prominent research interests among scientists. To better understand global volumetric change of ice sheets, NASA intends to launch Ice, Cloud and land Elevation Satellite-2 (ICESat-2) in 2017. ICESat-2 employs a high frequency photon-counting laser altimeter, which will provide significantly greater spatial sampling. However, the combined effects of sub-beam complex surfaces, as well as system effects on returning photon distribution have not been systematically studied. To better understand the effects of various system attributes and to help improve the theory behind lidar sensing of complex surfaces, an analytical model using a first principles 3-D Monte Carlo approach is developed to predict system performance. Based on the latest ICESat-2 design, this analytical model simulates photons which propagate from the laser transmitter to the scene, and reflected to the detector model. A radiometric model is also applied in the synthetic scene. Such an approach allows the study of surface elevation retrieval accuracy for landscapes, as well as surface reflectivities. It was found that ICESat-2 will have a higher precision on a smoother surface, and a surface with smaller diffuse albedo will on average result in smaller bias. Furthermore, an adaptive density-based algorithm is developed to detect the surface returns without any geometrical knowledge. This proposed approach is implemented using the aforementioned simulated data set, as well as airborne laser altimeter measurement. Qualitative and quantitative results are presented to show that smaller laser footprint, smoother surface, and lower noise rate will improve accuracy of ground height estimation. Meanwhile, reasonable detection accuracy can also be achieved in estimating both ground and canopy returns for data generated using Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. This proposed approach was found to be generally applicable for surface and canopy finding from photon-counting laser altimeter data

Open access data in polar and cryospheric remote sensing

This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use.A. Pope would like to acknowledge support from the Earth Observation Technology Cluster, a knowledge exchange project, funded by the Natural Environment Research Council (NERC) under its Technology Clusters Programme, the U.S. National Science Foundation Graduate Research Fellowship Program, Trinity College (Cambridge) and the Dartmouth Visiting Young Scientist program sponsored by the NASA New Hampshire Space Grant.This is the final published version. It's also available from MDPI at http://www.mdpi.com/2072-4292/6/7/6183

Spaceborne Lidar for Estimating Forest Biophysical Parameters

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and while this mission primarily serves to capture ice topography measurements of the earth’s surface, it also offers a phenomenal opportunity to estimate biophysical forest parameters at multiple spatial scales. This study served to develop approaches for utilizing ICESat-2 data over vegetated areas. The main objectives were to: (1) derive a simulated ICESat-2 photon-counting lidar (PCL) vegetation product using airborne lidar data and examine the use of simulated PCL metrics for modeling AGB and canopy cover, (2) create wall-to-wall AGB maps at 30-m spatial resolution and characterize AGB uncertainty by using simulated PCL-estimated AGB and predictor variables from Landsat data and derived products, and (3) investigate deep learning (DL) neural networks for producing an AGB product with ICESat-2, using simulated PCL-estimated AGB Landsat imagery, canopy cover and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using existing airborne lidar data and known ICESat-2 track locations for the first two years of the mission. Three scenarios were analyzed; 1) simulated data without the addition of noise, 2) processed simulated data for nighttime and 3) daytime scenarios. AGB model testing with no noise, nighttime and daytime scenarios resulted in R^2 values of 0.79, 0.79 and 0.63 respectively, with root mean square error (RMSE) values of 19.16 Mg/ha, 19.23 Mg/ha, and 25.35 Mg/ha. Canopy cover (4.6 m) models achieved R^2 values of 0.93, 0.75 and 0.63 and RMSE values of 6.36%, 12.33% and 15.01% for the no noise, nighttime and daytime scenarios respectively. Random Forest (RF) and deep neural network (DNN) models used with predicted AGB estimates and the mapped predictors exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha. Overall, findings from this study suggest the potential of ICESat-2 for estimating AGB and canopy cover and generating a wall-to-wall AGB product by adopting a combinatory approach with spectral metrics derived from Landsat optical imagery, canopy cover and land cover

Satellite altimeter remote sensing of ice caps

This thesis investigates the use of satellite altimetry techniques for measuring surface elevation changes of ice caps. Two satellite altimeters, Radar Altimeter 2 (RA-2) and Geoscience Laser Altimeter System (GLAS) are used to assess the surface elevation changes of three Arctic ice caps. This is the first time the RA-2 has been used to assess the elevation changes of ice caps - targets much smaller than the ice sheets which are the instrument’s primary land ice targets. Algorithms for the retrieval of elevation change rates over ice caps using data acquired by RA-2 and GLAS are presented. These algorithms form a part of a European Space Agency (ESA) glacier monitoring system GlobGlacier. A comparison of GLAS elevation data to those acquired by the RA-2 shows agreement between the two instruments. Surface elevation change rate estimates based on RA-2 are given for three ice caps: Devon Ice Cap in Arctic Canada (−0.09 ± 0.29 m/a), Flade Isblink in Greenland (0.03 ± 0.03 m/a) and Austfonna on Svalbard (0.33 ± 0.08 m/a). Based on RA-2 and GLAS measurements it is shown that the areas of Flade Isblink below the late summer snow line have been thinning whereas the areas above the late summer snow line have been thickening. Also GLAS observed dynamic thickening rates of more than 3 m/a are presented. On Flade Isblink and Austfonna RA-2 measurements are compared to surface mass balance (SMB) estimates from a regional atmospheric climate model RACMO2. The comparison shows that SMB is the driver of interannual surface elevation changes at Austfonna. In contrast the comparison reveals areas on Flade Isblink where ice dynamics have an important effect on the surface elevation. Furthermore, RACMO2 estimates of surface mass budget at Austfonna before the satellite altimeter era are presented. This thesis shows that both traditional radar and laser satellite altimetry can be used to quantify the response of ice caps to the changing climate. Direct altimeter measurements of surface elevation and, in consequence volume change of ice caps, can be used to improve their mass budget estimates

Lidar sampling for large-area forest characterization: A review

The ability to use digital remotely sensed data for forest inventory is often limited by the nature of the measures, which, with the exception of multi-angular or stereo observations, are largely insensitive to vertically distributed attributes. As a result, empirical estimates are typically made to characterize attributes such as height, volume, or biomass, with known asymptotic relationships as signal saturation occurs. Lidar (light detection and ranging) has emerged as a robust means to collect and subsequently characterize vertically distributed attributes. Lidar has been established as an appropriate data source for forest inventory purposes; however, large area monitoring and mapping activities with lidar remain challenging due to the logistics, costs, and data volumes involved.The use of lidar as a sampling tool for large-area estimation may mitigate some or all of these problems. A number of factors drive, and are common to, the use of airborne profiling, airborne scanning, and spaceborne lidar systems as sampling tools for measuring and monitoring forest resources across areas that range in size from tens of thousands to millions of square kilometers. In this communication, we present the case for lidar sampling as a means to enable timely and robust large-area characterizations. We briefly outline the nature of different lidar systems and data, followed by the theoretical and statistical underpinnings for lidar sampling. Current applications are presented and the future potential of using lidar in an integrated sampling framework for large area ecosystem characterization and monitoring is presented. We also include recommendations regarding statistics, lidar sampling schemes, applications (including data integration and stratification), and subsequent information generation. © 2012

An Overview of Requirements, Procedures and Current Advances in the Calibration/Validation of Radar Altimeters

Analysis of the radar echoes from a spaceborne altimeter gives information on sea surface height, wave height and windspeed, as well as other parameters over land and ice. The first spaceborne radar altimeter was pioneered on Skylab in 1974. Since then, there have been about 20 further missions, with several advances in the sophistication of hardware and complexity of processing with the aim of increased accuracy and precision. Because of that, the importance of regular and precise calibration and validation(“cal/val”) remains undiminished, especially with efforts to merge altimetric records from multiple missions spanning different domains and time periods. This special issue brings together 19 papers, with a focus on the recent missions (Jason-2, Jason-3, Sentinel-3A and HY-2B) as well as detailing the issues for anticipated future missions such as SWOT.This editorial provides a brief guide to the approaches and issues for cal/val of the various different derived parameters, including a synopsis of the papers in this special issue

Using a new generation of remote sensing to monitor Peru’s mountain glaciers

Remote sensing technologies are integral to monitoring mountain glaciers in a warming world. Tropical glaciers, of which around 70% are located in Peru, are particularly at risk as a result of climate warming. Satellite missions and field-based platforms have transformed understanding of the processes driving mountain glacier dynamics and the associated emergence of hazards (e.g. avalanches, floods, landslides), yet are seldom specialised to overcome the unique challenges of acquiring data in mountainous environments. A ‘new generation’ of remote sensing, marked by open access to powerful cloud computing and large datasets, high resolution satellite missions, and low-cost science-grade field sensors, looks to revolutionise the way we monitor the mountain cryosphere. In this thesis, three novel remote sensing techniques and their applicability towards monitoring the glaciers of the Peruvian Cordillera Vilcanota are examined. Using novel processing chains and image archives generated by the ASTER satellite, the first mass balance estimate of the Cordillera Vilcanota is calculated (-0.48 ± 0.07 m w.e. yr-1) and ELA change of up to 32.8 m per decade in the neighbouring Cordillera Vilcabamba is quantified. The performance of new satellite altimetry missions, Sentinel-3 and ICESat-2, are assessed, with the tracking mode of Sentinel-3 being a key limitation of the potential for its use over mountain environments. Although currently limited in its ability to extract widespread mass balance measurements over mountain glaciers, other applications for ICESat-2 in long-term monitoring of mountain glaciers include quantifying surface elevation change, identifying large accumulation events, and monitoring lake bathymetry. Finally, a novel low-cost method of performing timelapse photogrammetry using Raspberry Pi camera sensors is created and compared to 3D models generated by a UAV. Mean difference between the Raspberry Pi and UAV sensors is 0.31 ± 0.74 m, giving promise to the use of these sensors for long-term monitoring of recession and short-term warning of hazards at glacier calving fronts. Together, this ‘new generation’ of remote sensing looks to provide new glaciological insights and opportunities for regular monitoring of data-scarce mountainous regions. The techniques discussed in this thesis could benefit communities and societal programmes in rapidly deglaciating environments, including across the Cordillera Vilcanota