A Mass Balance Study of the West Antarctic Ice Sheet

The present state of the West Antarctic ice sheet (WAIS) is a prime concern of science, but its large size and remote location have limited the amount of reliable data that are available for mass balance calculations. The spatial pattern of mass balance for a 100-km2 portion of the WAIS is estimated by calculating the residual flux of ice through 1-km grid cells organized into a geographical information system (GIs). The input data used for this estimate include continent-scale compilations of ice thickness and snow accumulation rate measurements, and ground-based measurements of snow accumulation rate and ice velocity. The calculation was performed using different combinations of input data so that error sources could be identified. The largest sources of error were associated with the continent-scale compilations of accumulation rate and ice thickness. These errors are greatly reduced when using snow accumulation rates derived from ground-penetrating radar (GPR) surveys. The best results, which agree with two previous estimates, suggest that this area is nearly in balance. Results also indicate that the mass balance varies within this 100-km2 grid. In some portions of the grid, local variations in mass balance correspond with measured changes in ice velocity and snow accumulation rate. In other parts of the grid, the apparent spatial variability is attributed to errors in the ice thickness data. The results show that the demonstrated accuracy and spatial resolution of this high-resolution sampling approach are needed to understand the response of the entire West Antarctic ice sheet to recent changes in climate. However, the accuracy of the data compilations discussed above are examined using continuous, simultaneously recorded, ground-based measurements of ice-sheet surface topography, ice thickness, and snow accumulation rate that extend for hundreds of km beyond the grid at Byrd Station. Results from these analyses suggest that each of the compilations have larger errors than previously reported and therefore need to be improved before they are incorporated into estimates of WAIS mass balance

Doctor of Philosophy

dissertationRecent surface mass balance changes in space and time over the polar ice sheets need to be better constrained in order to estimate the ice-sheet contribution to sea-level rise. The mass balance of any ice body is obtained by subtracting mass losses from mass gains. In response to climate changes of the recent decades, ice-sheet mass losses have increased, making ice-sheet mass balance negative and raising sea level. In this work, I better quantify the mass gained by snowfall across the polar ice sheets; I target specific regions over both Greenland and West Antarctica where snow accumulation changes are occurring due to rising air temperature. Southeast Greenland receives 30% of the total snow accumulation of the Greenland ice sheet. In this work, I combine internal layers observed in ice-penetrating radar data with firn cores to derive the last 30 years of accumulation and to measure the spatial pattern of accumulation toward the southeast coastline. Below 1800 m elevation, in the percolation zone, significant surface melt is observed in the summer, which challenges both firn-core dating and internal-layer tracing. While firn-core drilling at 1500 m elevation, liquid water was found at ~20-m depth in a firn aquifer that persisted over the winter. The presence of this water filling deeper pore space in the firn was unexpected, and has a significant impact on the ice sheet thermal state and the estimate of mass balance made using satellite altimeters. Using a 400-MHz ice-penetrating radar, the extent of this widespread aquifer was mapped on the ground, and also more extensively from the air with a 750-MHz airborne radar as part of the NASA Operation IceBridge mission. Over three IceBridge flight campaigns (2011-2013), based on radar data, the firn aquifer is estimated to cover ~30,000 km2 area within the wet-snow zone of the ice sheet. I use repeated flightlines to understand the temporal variability of the water trapped in the firn aquifer and to simulate its lateral flow, following the gentle surface slope (< 1) and undulated topography of the ice sheet surface toward the ablation zone of the ice sheet. The fate of this water is currently unknown; water drainage into crevasses and at least partial runoff is inferred based on the analysis of radar profiles from different years. I also present results from a field expedition in West Antarctica, where data collection combined high-frequency (2-18 GHz) radar data and shallow (< 20 m) firn cores from Central West Antarctica, crossing the ice divide toward the Amundsen Sea. The radar-derived accumulation rates show a 75% increase (+0.20 m w.eq. y-1) of net snow accumulation from the ice divide, toward the Amundsen Sea for a 70-km transect, assuming annual isochrones being detected in the radar profile. On the Ross Sea side of the divide, with accumulation rates less than 0.25 m w.eq. y-1 and significant wind redistribution, only a multi-annual stratigraphy is detected in the radar profile. Using radar, I investigated the small-scale variability within a radius of ~1.5 km of one firn-core site, and I find that the averaged variation in accumulation-rate in this area is 0.1 m w.eq. y-1 in the upper 25-m of the firn column, which is 20% of the average accumulation rate

Study of Seasonal change and Water Stress Condition in Plant Leaf Using Polarimetric Lidar Measurement

Study of vegetation is of great importance to the improvement of agriculture and forest management. Although there have been various attempts to characterize vegetation using remote sensing techniques, polarimetric lidar is a novel remote sensing tool that has shown potential in vegetation remote sensing. In this thesis, a near-infrared polarimetric lidar at 1064 nm was used to investigate the effects of seasonal change and water stress condition on plant leaves. Two variables, time and water content, affected the plant leaf laser depolarization ratio measurement. The first study focused on the maple tree in order to figure out how seasonal change affected the maple leaf depolarization. Seasonal trendline was obtained and revealed an overall downward trend over time. In the second study, the leaves from maple, lemon, and rubber trees were investigated to study the effect of water stress on the depolarization ratio. It was discovered that the leaf depolarization ratio increased for more water content and went down for less water content. In addition, leaf samples were collected in the morning, afternoon, and evening, respectively, to study the diurnal change. Statistical analysis suggested that depolarization ratio did not change significantly for the different times of a day. It was suggested that the seasonal change had a greater effect on depolarization than the diurnal change. This study demonstrates that the near-infrared polarimetric lidar system has an ability to remotely characterize the vegetation internal conditions that may not be visible to the human eyes. Furthermore, the lidar system has the potential to differentiate the various plant species based on the depolarization ratio. In conclusion, the polarimetric lidar system at 1064-nm is an effective and sensitive enough remote sensing tool which can be widely used in active remote sensing

Toward Global Localization of Unmanned Aircraft Systems using Overhead Image Registration with Deep Learning Convolutional Neural Networks

Global localization, in which an unmanned aircraft system (UAS) estimates its unknown current location without access to its take-off location or other locational data from its flight path, is a challenging problem. This research brings together aspects from the remote sensing, geoinformatics, and machine learning disciplines by framing the global localization problem as a geospatial image registration problem in which overhead aerial and satellite imagery serve as a proxy for UAS imagery. A literature review is conducted covering the use of deep learning convolutional neural networks (DLCNN) with global localization and other related geospatial imagery applications. Differences between geospatial imagery taken from the overhead perspective and terrestrial imagery are discussed, as well as difficulties in using geospatial overhead imagery for image registration due to a lack of suitable machine learning datasets. Geospatial analysis is conducted to identify suitable areas for future UAS imagery collection. One of these areas, Jerusalem northeast (JNE) is selected as the area of interest (AOI) for this research. Multi-modal, multi-temporal, and multi-resolution geospatial overhead imagery is aggregated from a variety of publicly available sources and processed to create a controlled image dataset called Jerusalem northeast rural controlled imagery (JNE RCI). JNE RCI is tested with handcrafted feature-based methods SURF and SIFT and a non-handcrafted feature-based pre-trained fine-tuned VGG-16 DLCNN on coarse-grained image registration. Both handcrafted and non-handcrafted feature based methods had difficulty with the coarse-grained registration process. The format of JNE RCI is determined to be unsuitable for the coarse-grained registration process with DLCNNs and the process to create a new supervised machine learning dataset, Jerusalem northeast machine learning (JNE ML) is covered in detail. A multi-resolution grid based approach is used, where each grid cell ID is treated as the supervised training label for that respective resolution. Pre-trained fine-tuned VGG-16 DLCNNs, two custom architecture two-channel DLCNNs, and a custom chain DLCNN are trained on JNE ML for each spatial resolution of subimages in the dataset. All DLCNNs used could more accurately coarsely register the JNE ML subimages compared to the pre-trained fine-tuned VGG-16 DLCNN on JNE RCI. This shows the process for creating JNE ML is valid and is suitable for using machine learning with the coarse-grained registration problem. All custom architecture two-channel DLCNNs and the custom chain DLCNN were able to more accurately coarsely register the JNE ML subimages compared to the fine-tuned pre-trained VGG-16 approach. Both the two-channel custom DLCNNs and the chain DLCNN were able to generalize well to new imagery that these networks had not previously trained on. Through the contributions of this research, a foundation is laid for future work to be conducted on the UAS global localization problem within the rural forested JNE AOI

Chicora research contribution 312

This survey was done to assess the archaeological significance of the site

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided

Predicting glacier accumulation area distributions

A mass balance model based on energy balance at the terrain surface was developed and used to predict glacier accumulation areas in the Jotunheimen, Norway. Spatially distributed melt modelling used local climate and energy balance surfaces to drive predictions, derived from regional climate and topographic data. Predictions had a temporal resolution of 1 month and a spatial resolution of 100 m, which were able to simulate observed glacier accumulation area distributions. Data were stored and manipulated within a GIS and spatial trends and patterns within the data were explored. These trends guided the design of a suite of geomorphologically and climatologically significant variables which were used to simulate the observed spatial organisation of climatic variables, specifically temperature, precipitation and wind speed and direction. DEM quality was found as a critical factor in minimising error propagation. A new method of removing spatially and spectrally organised DEM error is presented using a fast Fourier transformation. This was successfully employed to remove error within the DEM minimising error propagation into model predictions. With no parameter fitting the modeled spatial distribution of snowcover showed good agreement with observed distributions. Topographic maps and a Landsat ETM+ image are used to validate the predictions and identify areas of over or under prediction. Topographically constrained glaciers are most effectively simulated, where aspect, gradient and altitude impose dominant controls on accumulation. Reflections on the causes of over or under prediction are presented and future research directions to address these are outlined. Sensitivity of snow accumulation to climatic and radiative variables was assessed. Results showed the mass balance of accumulation areas is most sensitive to air temperature and cloud cover parameterisations. The model was applied to reconstruct snow accumulation at the last glacial maximum and under IPCC warming scenarios to assess the sensitivity of melt to changing environmental conditions, which showed pronounced sensitivity to summer temperatures Low data requirements: regional climate and elevation data identify the model as a powerful tool for predicting the onset, duration and rate of melt for any geographical area