Xiao Cheng

Ocean-driven thinning enhances iceberg calving and retreat of Antarctic ice shelves

Significance The floating parts of the Antarctic ice sheet (“ice shelves”) help to hold back the flow of the grounded parts, determining the contribution to global sea level rise. Using satellite images, we measured, for the first time, all icebergs larger than 1 km2 calving from the entire Antarctic coastline, and the state of health of all the ice shelves. Some large ice shelves are growing while many smaller ice shelves are shrinking. We find high rates of iceberg calving from Antarctic ice shelves that are undergoing basal melt-induced thinning, which suggests the fate of ice shelves may be more sensitive to ocean forcing than previously thought. Iceberg calving from all Antarctic ice shelves has never been directly measured, despite playing a crucial role in ice sheet mass balance. Rapid changes to iceberg calving naturally arise from the sporadic detachment of large tabular bergs but can also be triggered by climate forcing. Here we provide a direct empirical estimate of mass loss due to iceberg calving and melting from Antarctic ice shelves. We find that between 2005 and 2011, the total mass loss due to iceberg calving of 755 ± 24 gigatonnes per year (Gt/y) is only half the total loss due to basal melt of 1516 ± 106 Gt/y. However, we observe widespread retreat of ice shelves that are currently thinning. Net mass loss due to iceberg calving for these ice shelves (302 ± 27 Gt/y) is comparable in magnitude to net mass loss due to basal melt (312 ± 14 Gt/y). Moreover, we find that iceberg calving from these decaying ice shelves is dominated by frequent calving events, which are distinct from the less frequent detachment of isolated tabular icebergs associated with ice shelves in neutral or positive mass balance regimes. Our results suggest that thinning associated with ocean-driven increased basal melt can trigger increased iceberg calving, implying that iceberg calving may play an overlooked role in the demise of shrinking ice shelves, and is more sensitive to ocean forcing than expected from steady state calving estimates.

Earth science applications of ICESat/GLAS: a review

The Ice, Cloud, and Land Elevation Satellite (ICESat) completed 19 successful campaigns for Earth observation missions following its launch in 2003. The Geoscience Laser Altimeter System (GLAS) on board ICESat provided data of high quality with unprecedented accuracy over the globe. The three laser sensors of GLAS acquired a large volume of data between 2003 and 2009. These data were used widely to detect changes in Greenland and Antarctic ice sheets and to determine forest heights, sea-ice freeboard heights and the distribution of cloud and aerosols. Here, we provide a review of these applications, describe the methodology involved in GLAS data processing and summarize some of the challenges to make better use of GLAS data. Other applications, including ice-sheet slope extraction, distinguishing between water, bare land, urban building and high forest, urban building height extraction, changes in glaciers and ice caps and water levels in lakes are discussed more briefly.

Automated Methods for Measuring DBH and Tree Heights with a Commercial Scanning Lidar

Accurate forest structural parameters are crucial to forest inventory, and modeling of the carbon cycle and wildlife habitat. Lidar (Light Detection and Ranging) is particularly suitable to the measurement of forest structural parameters. In this paper, we describe a pilot study to extract forest structural parameters, such as tree height, diameter at breast height (DBH), and position of individual tree using a terrestrial lidar (LMS-Z360i; Riegel, Inc.). The lidar was operated to acquire both vertical and horizontal scanning in the field in order to obtain a point cloud of the whole scene. An Iterative Closet Point (ICP) algorithm was introduced to obtain the transformation matrix of each range image and to mosaic multiple range images together. Based on the mosaiced data set, a variable scale and threshold filtering method was used to separate ground from the vegetation. Meanwhile, a Digital Elevation Model (DEM) and a Canopy Height Model (CHM) were generated from the classified point cloud. A stem detection algorithm was used to extract the location of individual trees. A slice above 1.3 m from the ground was extracted and rasterized. A circle fitting algorithm combined with the Hough transform was used to retrieve the DBH based on the rasterized grid. Tree heights were calculated using the height difference between the minimum and maximum Z values within the position of each individual tree with a 1 m buffer. All of the 26 trees were detected correctly, tree height and DBH were determined with a precision of 0.76 m and 3.4 cm, respectively, comparing with those visually measured in the lidar data. Our methods and results confirm that terrestrial lidar can provide nondestructive, high-resolution, and automatic determination of parameters required in forest inventory.

Improving measurement of forest structural parameters by co-registering of high resolution aerial imagery and low density LiDAR data.

Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data.

Settlement extraction in the North China Plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm

In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30–40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETMu2009+u2009) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30–32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85.

Lake Water Footprint Identification From Time-Series ICESat/GLAS Data

To provide high-quality data for time-series change detection of lake water level, an automatic and robust algorithm for lake water footprint (LWF) identification is developed. Based on the Ice, Cloud, and Land Elevation Satellite GLA14 data file, six parameters were taken as features of an algorithm for LWF identification, and they are elevation difference between adjacent footprints, waveform width, number of peaks, reflectivity, kurtosis, and skewness of laser echoes. The sensitivity of each parameter was discussed, and elevation difference between adjacent footprints was proved to be most effective. The algorithm was described as a combination of these six parameters, and the thresholds of each parameter were set through statistics of LWF covering Peiku Co in Tibet, China, from 2003 to 2009. The performance of this classification algorithm was evaluated by the users accuracy and producers accuracy. Greater than 94% is achieved for all four tested lakes with 97% being the best result of producers accuracy, and the users accuracy ranges from 97.9% to 90% for these four lakes.

Mapping blue-ice areas in Antarctica using ETM+ and MODIS data

Abstract Blue-ice areas (BIAs) and their geographical distribution in Antarctica were mapped using Landsat-7 ETM+ images with 15 m spatial resolution obtained during the 1999–2003 austral summers and covering the area north of 82.5° S, and a snow grain-size image of the MODIS-based Mosaic of Antarctica (MOA) dataset with 125 m grid spacing acquired during the 2003/04 austral summer from 82.5°S to the South Pole. A map of BIAs was created with algorithms of thresholds based on band ratio and reflectance for ETM+ data and thresholds based on snow grain size for the MOA dataset. The underlying principle is that blue ice can be separated from snow or rock by their spectral discrepancies and by different grain sizes of snow and ice. We estimate the total area of BIAs in Antarctica during the data acquisition period is 234 549 km2, or 1.67% of the area of the continent. Blue ice is scattered widely over the continent but is generally located in coastal or mountainous regions. The BIA dataset presented in this study is the first map covering the entire Antarctic continent sourced solely from ETM+ and MODIS data. This dataset can potentially benefit other studies in glaciology, meteorology, climatology and paleoclimate, meteorite collection and airstrip site selection.

Sea-ice conditions in the Adélie Depression, Antarctica, during besetment of the icebreaker RV Xuelong

Abstract During the 30th Chinese Antarctic Expedition in 2013/14, the Chinese icebreaker RV Xuelong answered a rescue call from the Russian RV Akademik Shokalskiy. While assisting the repatriation of personnel from the Russian vessel to the Australian RV Aurora Australis, RV Xuelong itself became entrapped within the compacted ice in the Adélie Depression region. Analysis of MODIS and SAR imagery provides a detailed description of the regional sea-ice conditions which led to the 6 day long besetment of RV Xuelong. The remotely sensed imagery revealed four stages of sea-ice characteristics during the entrapment: the gathering, compaction, dispersion and calving stages. Four factors characterizing the local sea-ice conditions during late December 2013 and early January 2014 were identified: surface component of the coastal current; near-surface wind; ocean tides; and surface air temperature. This study demonstrates that shipping activity in ice-invested waters should be underpinned by general knowledge of the ice situation. In addition, during such activity high spatiotemporal resolution remotely sensed data should be acquired regularly to monitor local and regional sea-ice changes with a view to avoiding the besetment of vessels.

Opportunities and challenges of applications of satellite-derived sun-induced fluorescence at relatively high spatial resolution

Estimating gross primary production (GPP) regionally and globally remains challenging despite its primary role in driving ecosystem productivity and carbon cycling. Recently, satellite-derived sun-induced fluorescence (SIF) provides an alternative approach to investigate GPP from space. However, our ability to apply SIF to estimating GPP at large scales is still lacking, primarily because the SIF-GPP relationships at various spatial and temporal scales are not fully understood. The coarse spatial representativeness (around 0.5° or coarser) of previous satellite-derived SIF data makes it difficult to compare and validate with eddy covariance (EC) based GPP measurements. Orbiting Carbon Observatory-2 (OCO-2) has shown prospects in providing SIF at significantly improved spatial resolutions (around 1.3km by 2.25km) that are comparable to ground-based GPP measurements. However, OCO-2 operates at a 16-day revisiting schedule with a sparse spatial sampling strategy. We found that for most EC sites, the observations of OCO-2 passing through were extremely limited. The average number of successfully retrieved SIF by OCO-2 encompassing each site within a year was only 3.21 from 2015 to 2016. For an EC site with high companion OCO-2 coverages, we found a strong correlation between GPP and SIF. Despite challenges, the emerging high-spatial-resolution SIF data provide unprecedented opportunities to estimate GPP over time and space and its underlying mechanism. We recommend that to fully use the satellite-derived SIF data, a research agenda is critically needed to improve our understanding of the relationship between SIF and GPP across biomes, ecosystems, and even species. We advocate maintaining and upgrading current EC sites and adding ground-based SIF measurements to provide another scale of SIF observations. We also suggest constructions of new EC sites taking into consideration the scientific benefits that can be gained by locating sites within the belts within OCO-2 or other satellite-derived SIF missions.

Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica

Sea ice adversely impacts nautical, logistical and scientific missions in polar regions. Ship navigation benefits from up-to-date sea ice analyses at both regional and local scales. This study presents a satellite-based sea ice navigation system (SatSINS) that integrates observations and scientific output from remote sensing and meteorological data to develop optimum marine navigational routes in sea ice-covered waters, especially in areas where operational ice information is usually scarce. The system and its applications are presented in the context of a decision-making process to optimize the routing of the RV Xuelong during her passage through Prydz Bay, East Antarctica during three trips in the austral spring of 2011–2013. The study assesses scientifically-generated remote sensing ice parameters for their operational use in marine navigation. Evaluation criteria involve identification of priority parameters, their spatio-temporal requirements in relation to navigational needs, and their level of accuracy in conjunction with the severity of ice conditions. Coarse-resolution ice concentration maps are sufficient to delineate ice edge and develop a safe route when ice concentration is less than 70%, provided that ice dynamics, estimated from examining the cyclonic pattern, is not severe. Otherwise, fine-resolution radar data should be used to identify and avoid deformed ice. Satellite data lagging one day behind the actual location of the ship was sufficient in most cases although the proposed route may have to be adjusted. To evaluate the utility of SatSINS, deviation of the actual route from the proposed route was calculated and found to range between 165 m to about 16.0 km with standard deviations of 2.8–6.1 km. Growth of land-fast ice has proven to be an essential component of the system as it was estimated using a thermodynamic model with input from a meteorological station.