The Ice, Cloud, and land Satellite-2 (ICESat-2) is planned to launch in 2016 carrying the Advanced Topographic Laser Altimeter System (ATLAS). ATLAS will be the first space-based photon-counting laser altimeter to be put into operation, and is tasked with observing the Earth’s ice sheets, sea ice, and vegetation. The environment in which ATLAS will be operating is expected to introduce a significant amount of noise into the received signal; this necessitates that a set of onboard Receiver Algorithms be developed to reduce the data volume and data rate to acceptable levels while still transmitting the relevant ranging data. The algorithms make use of signal processing techniques, along with three databases, the Digital Elevation Model (DEM), the Digital Relief Map (DRM), and the Surface Reference Mask (SRM), to find the signal and determine the appropriate dynamic range of vertical data surrounding the surface for downlink. The focus of this study is the development of the DEM and DRM databases. A number of elevation data sets are examined for use as inputs for the databases. No global data sets of sufficient quality and resolution are available for the development of the project, so best-available regional elevation data sets were selected instead. Software was developed in MATLAB to produce the DEM and DRM data bases from the input data sets. A method for calculating relief from a gridded elevation data set along the flight path of a satellite was developed for the generation of relief maps used to create the DRM. Global DEM and DRM databases were produced by mosaicking individual DEM and DRM tiles from each input data set into global grids. A technique was developed to determine the accuracy of the DRM by using ICESat ground elevations to evaluate the accuracy of an input elevation data set. By comparing values of DRM accuracy to values of DRM relief, estimates of DRM accuracy as a function of relief magnitude were determined and used to define values of DRM padding in the receiver algorithm. / text

This study investigates pine heights by age for actively managed stands in the southeastern U.S. using ICESat-2 ATL08-derived height data and maps derived from the Landsat time series. We intersected ICESat-2 ground tracks with locations of pine plantations and the Landscape Change Monitoring System (LCMS) Fast Loss product to identify previously clear-cut pine plantations. We subtracted the LCMS Fast Loss year from the date of the ICESat-2 acquisition to determine plantation age at the time of the height measurement. We stratified the data for management intensity, where stands that experience both thinning and harvesting were considered actively managed. The goal was to develop age-height relationships across the region to characterize better the impact of management on productivity and site index. This research involved the analysis of over 137,998 ICESat-2 ATL08 segments in actively managed pine stands in the U.S. Southeast. We compared a subset of ICESat-2 heights with heights derived from airborne laser scanning acquisitions (ALS) available through the USGS 3D Elevation Program. The resulting R2 was 0.82, giving us confidence in the ICESat-2 ATL08-derived forest heights. Then, through data processing and analysis, we successfully stratified the spatial patterns of ICESat-2 ATL08 heights in the southeastern region. These patterns provided insights into the distribution and variability of forest heights across the region, contributing to informed decisions in forest management. We identified some challenges in predicting pine stand age through Landsat-derived disturbance products. We found that LCMS Fast Loss labels some heavy thins as a ‘Fast Loss,’ in addition to stand-clearing disturbances like clear-cuts, adding noise to our estimation of stand age. To overcome this issue, we employed a robust model of the logarithm of heights with a reciprocal of age using a random sample consensus (RANSAC) model to calculate site indices at base age 25 (years). Our results showed the site index for the region at a base age of 25 years is 20.1 m with a model R2 of 0.91. We compared the ICESat-2-derived site index with the FIA-derived site index to see the robustness of our results. Then, the modeled site index values were used to produce a map at a base age of 25 years for the U.S. Southeast, offering insights into spatial differences in regional forest productivity. The results of this study have important implications for ecological research, forest management, and well-informed decision-making. Insights into the distribution and trends of actively managed forest heights in the Southeast are gained from studying the vast dataset, allowing for more efficient land management and conservation initiatives. In actively manage stands, our site index equation improves the ability to anticipate site productivity and estimate future timber outputs. The difficulties with age estimation that have been observed highlight the need for better methods for mapping disturbances using remote sensing in forests that use thinning as a silvicultural prescription. / US Forest Service, joint venture agreement 20-JV-11330145-037, and the USDA Mclntire-Stennis Formula Grant program, accession number 7003904, “Precision forestry for southern pine carbon monitoring.” / Master of Science

Satellitenaltimetrie ist ein Verfahren zur flächendeckenden Beobachtung von Oberflächenhöhen. In den Polarregionen spielen die daraus ableitbaren Höhenänderungsraten eine zentrale Rolle bei der Abschätzung von Massenbilanzen kontinentaler Eisschilde. Aktuell befindet sich eine Vielzahl solcher Altimetriesatelliten im Orbit. In Abhängigkeit vom Messverfahren und verbauten Instruments unterscheiden sich gemessene Höhen sowie der Einfluss systematischer Fehler. In dieser Masterarbeit werden für die Region Nordostgrönland Beobachtungen verschiedener Sensoren prozessiert, vergleichend interpretiert und teilweise validiert. Auswertungen erfolgen anhand digitaler Geländemodelle (DGM), wobei neben bereits etablierten Datensätzen ein Workflow zur automatisierten DGM-Generierung anhand von sehr hochaufgelösten optischen Satellitenaufnahmen vorgestellt sowie angewandt wird. Darauf aufbauend findet eine umfassende Validierung der Eisoberflächenhöhen ATL06 des im September 2018 gestarteten ICESat-2 statt. Es wurde gezeigt, dass keine signifikanten systematischen Abweichungen unter den einzelnen Laserstrahlen sowie zwischen aufsteigenden und absteigenden Satellitenspuren bestehen. Auf dem Eisschild wurden Abweichungen bis zu 10 cm und in den Küstengletscherregionen bis zu 70 cm errechnet, wobei das Genauigkeitsniveau von ICESat-2 über dem der Referenzen liegt. Anhand eines ersten Anwendungsbeispieles konnte die sehr gute Eignung von ICESat-2 zur Co-Registrierung von DGMs gezeigt werden. Durch Hinzunahme von Beobachtungen aktueller Radaraltimeter wurden sämtliche über Eisschilden eingesetzte Altimetriesensoren und Messverfahren vergleichend analysiert. Der zeitlich variable Einfluss der Firnstruktur ist von der Wellenlänge des Messsignals abhängig und kann durch Nutzung eines entsprechenden Retracking-Verfahrens minimiert werden. Korrektionen auf Grundlage verschiedener Parameter des Rückkehrsignals sind für den grönländischen Eisschild ungeeignet. Untersuchungen des Topografieeinflusses auf die Beobachtung zeigen ein enormes Fehlerpotential pulslimitierter Radarsysteme ab Geländeneigungen von 0,3°, wohingegen das Laseraltimeter ICESat-2 eine präzise Vermessung rauer Gebirgs- und Gletscherregionen ermöglicht.

<p>The spaceborne lidar Ice, Cloud, and land Elevation Satellite (ICESat)-2 provides the ATL13 data product for inland water bodies. However, its quality characteristics are not yet fully understood. This study presents a robust method for extracting lake water level data and makes a comprehensive evaluation on the determined water levels. The selected study areas are Lake Huron and Lake Superior, which are part of the Great Lakes. The extracted water levels from ATL13 over a period of four years are validated by using the field measurements at the closest NOAA hydrological stations. The evaluation is carried out in terms of data specifications, wind speed, frozen precipitation, distance of photon segments to hydrological stations, data acquisition time, and beam intensity. The determined water levels are then further used for seasonal monitoring and modeling of water surface. This work demonstrates the critical need for outlier removal and the capability of the ATL13 data. A total bias of 9 - 10 cm is found in the ATL13 product. It is found that frozen precipitation can lead to an overestimation (~ 5 cm) of the water level. However, the uncertainty of water level determination is not found to be significantly related to the laser beam intensity and data acquisition time. We expect that these findings will be valuable for users employing the ATL13 inland water body product and for developers producing future versions of the ATL13 product.</p>

The Arctic cryosphere is experiencing a higher rate of warming compared to the rest of the world due to Arctic amplification. As glacier elevation change provide reliable evidence of climate change it is routinely measured by satellite altimeters. Satellite altimetry, while a valuable tool for monitoring elevation change over time, is subject to inherent uncertainties caused by, among other factors, the small scale surface roughness of the target surfaces. Previous studies have identified surface roughness as a key source of uncertainty when measuring sea ice freeboard and studies suggest the surface roughness strongly influences the Synthetic Aperture Radar (SAR) signatures of sea ice. Similar studies over snow- and glacier surfaces, are rare. In this context, we attempt to conduct a small scale calibration and validation (cal/val) campaign over glacier surfaces, using the ideal location and infrastructure of the University Centre in Svalbard. We demonstrate the process, from planning through field data collection and data analysis. By doing so, we identify good as well as bad practices. Using high resolution in-situ LiDAR data, collected under two ICESat-2 (IS2) overpasses in Svalbard we generated Digital Elevation Models (DEM) and calculated surface roughness estimates across glacier- and snow surfaces. The surface roughness was quantified by calculating the Root Mean Square (RMS) of deviations from the overall topography of the surfaces. The DEMs were used for direct comparison with the satellite elevation retrievals and the observed elevation differences were tested for correlation with surface roughness at different length scales. We then investigated the effect of surface roughness on the photon cloud of the lower level ATL03 ICESat-2 data products, by quantifying the precision in the data. We found little to no correlation between RMS roughness and the observed elevation differences between in-situ and satellite data sets, possibly explained by errors in georeferencing the DEMs. We show moderate to strong correlation between photon cloud precision and along- and across-track absolute surface slopes, with correlation coefficients of 0.6–0.8. Correlation between photon cloud precision and RMS roughness was found, with a maximum correlation coefficient of 0.9 for a roughness length scale of 1m. The results suggest IS2 is sensitive to surface roughness at similar length scales but we identify a need for more data, covering a wider range of surfaces and potential roughness scenarios, to draw strong conclusions. We demonstrate how a small team can carry out a cal/val campaign in the high arctic and collect coincident data under satellite overpasses, data which is typically rare for the remote high Arctic regions.

This study aimed at evaluating the agreement of spaceborne Light Detection and Ranging (lidar) ICESat-2 canopy height with Airborne Laser Scanning (ALS) derived canopy height to inform about the performance of ICESat-2 canopy height metrics and understand its uncertainties and utilities. The agreement was assessed for different forest types, physiographic regions, a range of percent canopy cover, and diverse disturbance histories. Results of this study suggest that best agreements are found using strong beam data collected at night for canopy height retrieval using ICESat-2. The ICESat-2 showed great potential for estimating canopy heights, particularly in evergreen forests with high canopy cover. Statistical models were developed using fixed-effects and mixed-effects modeling approaches to predict ALS canopy height metrics using ICESat-2 parameters and other attributes. Overall, ICESat-2 showed good agreement with ALS canopy height and showed its predictive ability to characterize canopy height. The outcome of this study will help the scientific community understand the capabilities and limitations of ICESat-2 canopy heights; the study also provides a new approach to obtain wall-to-wall ALS standard canopy height maps at landscape level.