Published Research

The following references cite studies that used ICESat data from NSIDC. Please contact User Services if you have a reference you would like to share on this page.

2017

Bye, I. J., et al. 2017. Estimating forest canopy parameters from satellite waveform LiDAR by inversion of the FLIGHT three-dimensional radiative transfer model. Remote Sensing of Environment 188: 177–189. doi: http://dx.doi.org/10.1016/j.rse.2016.10.048.

Chao, Nengfang, et al. 2017. Decline of Geladandong Glacier Elevation in Yangtze River’s Source Region: Detection by ICESat and Assessment by Hydroclimatic Data. Remote Sensing 9(1). Art. #75. doi: http://dx.doi.org/10.3390/rs9010075.

Chi, Hong, et al. 2017. Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data. Remote Sensing 9(7). Art. #707. doi: http://dx.doi.org/10.3390/rs9070707.

Chu, Thuan, and Karl-Erich Lindenschmidt. 2017. Comparison and Validation of Digital Elevation Models Derived from InSAR for a Flat Inland Delta in the High Latitudes of Northern Canada. Canadian Journal of Remote Sensing 43(2). doi: http://dx.doi.org/10.1080/07038992.2017.1286936.

Dhanda, P., et al. 2017. Optimising spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms. Progress in Physical Geography. doi: http://dx.doi.org/10.1177/0309133317693443.

Dhanda, P., et al. 2017. Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms . Progress in Physical Geography 41(3): 247-267. doi: http://dx.doi.org/10.1177/0309133317693443.

Ghosh, S., and M. D. Behera. 2017. Forest canopy height estimation using satellite laser altimetry: a case study in the Western Ghats, India. Applied Geomatics: 1-18. doi: http://dx.doi.org/10.1007/s12518-017-0190-2.

Ghosh, S., et al. 2017. Land Cover Classification Using ICESat/GLAS Full Waveform Data. Journal of the Indian Society of Remote Sensing 45(2): 327-335. doi: http://dx.doi.org/10.1007/s12524-016-0602-5.

Hajj, M. E., et al. 2017. Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas. Remote Sensing 9(3): Art. #213. doi: http://dx.doi.org/10.3390/rs9030213.

Holm, Sören, Ross Nelson, and Göran Ståhla. 2017. Hybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar. Remote Sensing of Environment 197: 85-97. doi: http://dx.doi.org/10.1016/j.rse.2017.04.004.

Huang, Huabing, et al. 2017. Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data. ISPRS Journal of Photogrammetry and Remote Sensing 129: 189-199. doi: http://dx.doi.org/10.1016/j.isprsjprs.2017.04.020.

Huber, Jacqueline, et al. 2017. A complete glacier inventory of the Antarctic Peninsula based on Landsat 7 images from 2000 to 2002 and other preexisting data sets. Earth System Science Data 9(1): 115-131. doi: http://dx.doi.org/10.5194/essd-9-115-2017.

Jin, Shuanggen, T. Y. Zhang, and F. Zou. 2017. Glacial density and GIA in Alaska estimated from ICESat, GPS and GRACE measurements. Journal of Geophysical Research - Earth Surface 122(1): 76-90. doi: http://dx.doi.org/10.1002/2016JF003926.

Liu, Guang, et al. 2017. Monitoring elevation change of glaciers on Geladandong Mountain using TanDEM-X SAR interferometry. Journal of Mountain Science 14(5): 859-869. doi: http://dx.doi.org/10.1007/s11629-016-3992-5.

Liu, Kaili, et al. 2017. Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data. 9(4): Art. #341. doi: http://dx.doi.org/10.3390/rs9040341.

Ma, Yue, et al. 2017. Waveform width of a satellite laser altimeter illuminating on the sea surface . Applied Optics 56(22): 6130-6137. doi: http://dx.doi.org/10.1364/AO.56.006130.

Mahoney, Craig, et al. 2017. Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems. Remote Sensing 9(1). Art #59. doi: http://dx.doi.org/10.3390/rs9010059.

Montesano, Paul M., et al. 2017. The use of sun elevation angle for stereogrammetric boreal forest height in open canopies. Remote Sensing of Environment 196: 76-88. doi: http://dx.doi.org/10.1016/j.rse.2017.04.024.

Neelmeijer, Julia, Mahdi Motagh, and Bodo Bookhagen. 2017. High-resolution digital elevation models from single-pass TanDEM-X interferometry over mountainous regions: A case study of Inylchek Glacier, Central Asia. ISPRS Journal of Photogrammetry and Remote Sensing 130: 108-121. doi: http://dx.doi.org/10.1016/j.isprsjprs.2017.05.011.

Nelson, Ross, et al. 2017. Lidar-based estimates of aboveground biomass in the continental US and Mexico using ground, airborne, and satellite observations. Remote Sensing of Environment 188: 127-140. doi: http://dx.doi.org/10.1016/j.rse.2016.10.038.

Phan, Vu Hien, Roderik Lindenbergh, and Massimo Menenti. 2017. Assessing Orographic Variability in Glacial Thickness Changes at the Tibetan Plateau Using ICESat Laser Altimetry. Remote Sensing 9(2). Art. #160. doi: http://dx.doi.org/10.3390/rs9020160.

Price, Stephen F., et al. 2017. An ice sheet model validation framework for the Greenland ice sheet . Geoscientific Model Development 10(1): 255-270. doi: http://dx.doi.org/10.5194/gmd-10-255-2017.

Qiao, Baojin, and Liping Zhu. 2017. Differences and cause analysis of changes in lakes of different supply types in the north-western Tibetan Plateau. Hydrological Processes 31(15): 2752–2763. doi: http://dx.doi.org/10.1002/hyp.11215.

Qureshi, Muhammad Ateeq, et al. 2017. Glacier status during the period 1973–2014 in the Hunza Basin, Western Karakoram. Quaternary International 444A: 125-136. doi: http://dx.doi.org/10.1016/j.quaint.2016.08.029.

Rius, Antonio, et al. 2017. Feasibility of GNSS-R Ice Sheet Altimetry in Greenland Using TDS-1. Remote Sensing 9(7): Art. #742. doi: http://dx.doi.org/10.3390/rs9070742.

Rizzoli, Paola, et al. 2017. Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data. Remote Sensing 9(4): Art. #315. doi: http://dx.doi.org/10.3390/rs9040315.

Su, Yanjun, Qin, and Qinghua Guo. 2017. The Use of LiDAR in Multi-Scale Forestry Applications. . Ph. D. U. of California, Merced.

Tang, Hao, and Ralph Dubayah. 2017. Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure. PNAS 14(10): 2640-2644. doi: http://dx.doi.org/10.1073/pnas.1616943114.

Xiong, Siting, Jan-Peter Muller, and Gang Li. 2017. The Application of ALOS/PALSAR InSAR to Measure Subsurface Penetration Depths in Deserts. Remote Sensing 9(6). Art. #638. doi: http://dx.doi.org/10.3390/rs9060638.

Yue, Linwei, et al. 2017. High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations. ISPRS Journal of Photogrammetry and Remote Sensing 123: 20-34. doi: http://dx.doi.org/10.1016/j.isprsjprs.2016.11.002.

2016

Alley, Karen E., et al. 2016. Impacts of warm water on Antarctic ice shelf stability through basal channel formation. Nature Geoscience 9(4): 290-293. doi: http://dx.doi.org/10.1038/ngeo2675.

Christianson, Knut, et al. 2016. Basal conditions at the grounding zone of Whillans Ice Stream, West Antarctica, from ice-penetrating radar. Journal of Geophysical Research - Earth Surface 121(11): 1954–1983. doi: http://dx.doi.org/10.1002/2015JF003806.

Crétaux, J. F., et al. 2016. Lake Volume Monitoring from Space. Surveys in Geophysics 37(2): 269-305. doi: http://dx.doi.org/10.1007/s10712-016-9362-6.

de Moura, Yhasmin Mendes, et al. 2016. Scaling estimates of vegetation structure in Amazonian tropical forests using multi-angle MODIS observations. International Journal of Applied Earth Observation and Geoinformation 52: 580-590. doi: http://dx.doi.org/10.1016/j.jag.2016.07.017.

Du, Xiaoping, et al. 2016. Vertical accuracy assessment of freely available digital elevation models over low-lying coastal plains. International Journal of Digital Earth 9(3): 252-271. doi: http://dx.doi.org/10.1080/17538947.2015.1026853.

Fayad, Ibrahim, et al. 2016. Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana. Remote Sensing 8(3). Art. #240. doi: http://dx.doi.org/10.3390/rs8030240.

Fayad, Ibrahim, et al. 2016. Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data. International Journal of Applied Earth Observation and Geoinformation 52: 502-514. doi: http://dx.doi.org/10.1016/j.jag.2016.07.015.

Feng, L., and J.-P. Muller. 2016. Icesat validation of tandem-X I-DEMs. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic XLI-B4: 129-136. doi: http://dx.doi.org/10.5194/isprsarchives-XLI-B4-129-2016.

Gwenzi, David, and Michael Andrew Lefsky. 2016. Spatial Modeling of Lidar-Derived Woody Biomass Estimates Collected Along Transects in a Heterogeneous Savanna Landscap. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(1): 372-384. doi: http://dx.doi.org/10.1109/JSTARS.2016.2582148.

Gwenzi, David, and Michael Andrew Lefsky. 2016. Spatial Modeling of Lidar-Derived Woody Biomass Estimates Collected Along Transects in a Heterogeneous Savanna Landscape. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(1): 372-384. doi: http://dx.doi.org/10.1109/JSTARS.2016.2582148.

Hansen, Matthew C., et al. 2016. Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data. Remote Sensing of Environment 185: 221–232. doi: http://dx.doi.org/10.1016/j.rse.2016.02.023.

Jawak, Shridhar D., and Alvarinho J. Luis. 2016. Generation of a precise DEM by interactive synthesis of multi-temporal elevation datasets: a case study of Schirmacher Oasis, East Antarctica . Proceedings of SPIE 9877. Art. #98772E. doi: http://dx.doi.org/10.1117/12.2223609.

Jawat, Shridhar D., and Alvarinho J. Luis. 2016. Generation of a Precise DEM by interactive synthesis of multitemporal elevation datasets: a case study of Schirmacher Oasis, East Antarctica. Land Surface and Cryosphere Remote Sensing III edited by Reza Khanbilvardi, Ashwagosh Ganju, A. S. Rajawat, Jing M. Chen. New Dehli: SPIE, Art. #98772E.. doi: http://dx.doi.org/10.1117/12.2223609.

Ke, Linghong, et al. 2016. Remote sensing of glacier distribution and change over the Qinghai-Tibet Plateau. Earth Observation and Remote Sensing Applications (EORSA), 2016 4th International Workshop on: 442-446. doi: http://dx.doi.org/10.1109/EORSA.2016.7552847.

Ke, Linghong. 2016. Remote sensing of mountain glaciers over the Qinghai-Tibet Plateau. . Ph. D. Hong Kong Polytechnic University.

Kern, Stefan, and Burcu Ozsoy-Çiçek. 2016. Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches. Remote Sensing 8(6). Art. #450. doi: http://dx.doi.org/10.3390/rs8060450.

Kern, Stefan, Burcu Ozsoy-Çiçek, and Anthony P. Worby. 2016. Antarctic Sea-Ice Thickness Retrieval from ICESat: Inter-Comparison of Different Approaches. Remote Sensing 8(7). Art. #538. doi: http://dx.doi.org/10.3390/rs8070538.

Khan, Shfaqat A., et al. 2016. Geodetic measurements reveal similarities between post–Last Glacial Maximum and present-day mass loss from the Greenland ice sheet. Science Advances 2(9). Art. #e1600931. doi: http://dx.doi.org/10.1126/sciadv.e1600931.

Kim, Byeong-Hoon, et al. 2016. Active subglacial lakes and channelized water flow beneath the Kamb Ice Stream. The Cryosphere 10(6): 2971–2980. doi: http://dx.doi.org/10.5194/tc-10-2971-2016.

Li, Guoyuan, et al. 2016. Improve the ZY-3 Accuracy Using ICESat/GLAS Laser Altimeter Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic 41-B1. Prague: ISPRS, 37-42. doi: http://dx.doi.org/10.5194/isprs-archives-XLI-B1-37-2016.

Li, Guoyuan, et al. 2016. ZY-3 Block adjustment supported by glas laser altimetry data. Photogrammetric Record 31(153): 88-107. doi: http://dx.doi.org/10.1111/phor.12138.

Li, Rongxing, et al. 2016. Quality assessment of existing antarctic remote sensing products. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing: 3481-3484. doi: http://dx.doi.org/10.1109/IGARSS.2016.7729900.

Li, Xiaolu, Kai Xu, and Lijun Xu. 2016. Within-footprint roughness measurements using ICESat/GLAS waveform and LVIS elevation. Measurement Science and Technology 27(12). Art. #125012. doi: http://dx.doi.org/10.1088/0957-0233/27/12/125012.

Li, Xiaolu, mand Lijun Xu. 2016. Surface slope and roughness measurement using ICESat/GLAS elevation and laser waveform. Measurement Science and Technology 27(9): Art. #095202. doi: http://dx.doi.org/10.1088/0957-0233/27/9/095202.

Li, Zhiguo, et al. 2016. Changes in glacier extent and surface elevations in the Depuchangdake region of northwestern Tibet, China. Quaternary Research 85(1): 25-33. doi: http://dx.doi.org/10.1016/j.yqres.2015.12.005.

Liu, Caixia, et al. 2016. The importance of data type, laser spot density and modelling method for vegetation height mapping in continental China. International Journal of Remote Sensing 37(24): 6127-6148. doi: http://dx.doi.org/10.1080/01431161.2016.1252472.

Liu, Shijie, et al. 2016. An Alternative Approach for Registration of High-Resolution Satellite Optical Imagery and ICESat Laser Altimetry Data. Sensors 16(12). Art. #2008. doi: http://dx.doi.org/10.3390/s16122008.

Magruder, Lori, Holly Leigh, and Amy Neuenschwander. 2016. Evaluation of terrain and canopy height products in central African tropical forests. International Journal of Remote Sensing 37(22): 5365-5387. doi: http://dx.doi.org/10.1080/01431161.2016.1232870.

Mahoney, Craig, et al. 2016. Continental-Scale Canopy Height Modeling by Integrating National, Spaceborne, and Airborne LiDAR Data. Canadian Journal of Remote Sensing 42(5): 574-590. doi: http://dx.doi.org/10.1080/07038992.2016.1196580.

Marsh, Oliver J., et al. 2016. High basal melting forming a channel at the grounding line of Ross Ice Shelf, Antarctica. Geophysical Research Letters 43(1): 250-255. doi: http://dx.doi.org/10.1002/2015GL066612.

Montesano, Paul M., et al. 2016. Spaceborne potential for examining taiga–tundra ecotone form and vulnerability. Biogeosciences 13(13): 3847-3861. doi: http://dx.doi.org/10.5194/bg-13-3847-2016.

Muhammad, Sher, and Lide Tian. 2016. Changes in the ablation zones of glaciers in the western Himalaya and the Karakoram between 1972 and 2015. Remote Sensing of Environment 187: 505-512. doi: http://dx.doi.org/10.1016/j.rse.2016.10.034.

O'Loughlin, Fiachra, et al. 2016. ICESat-derived inland water surface spot heights. Water Resources Research 524): 3276–3284. doi: http://dx.doi.org/10.1002/2015WR018237.

O'Loughlin, Fiachra, et al. 2016. A multi-sensor approach towards a global vegetation corrected SRTM DEM product. Remote Sensing of Environment 182: 49-59. doi: http://dx.doi.org/10.1016/j.rse.2016.04.018.

Pandey, Pratima, et al. 2016. Qualitative and quantitative assessment of TanDEM-X DEM over western Himalayan glaciated terrain. Geocarta International. doi: http://dx.doi.org/10.1080/10106049.2016.1155655.

Satge, Frédéric, et al. 2016. Absolute and relative height-pixel accuracy of SRTM-GL1 over the South American Andean Plateau. ISPRS Journal of Photogrammetry and Remote Sensing 121: 157-166. doi: http://dx.doi.org/10.1016/j.isprsjprs.2016.09.003.

Seehaus, Thorsten C., et al. 2016. Dynamic Response of Sjögren Inlet Glaciers, Antarctic Peninsula, to Ice Shelf Breakup Derived from Multi-Mission Remote Sensing Time Series. Frontiers in Earth Science 14. doi: http://dx.doi.org/10.3389/feart.2016.00066.

Shean, David, et al. 2016. An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery. SPRS Journal of Photogrammetry and Remote Sensing 116: 101-117. doi: http://dx.doi.org/10.1016/j.isprsjprs.2016.03.012.

Shuman, Christopher A., Ted Scambos, and Etienne Berthier. 2016. Ice loss processes in the Seal Nunataks ice shelf region from satellite altimetry and imagery. Annals of Glaciology 57(73): 94-104. doi: http://dx.doi.org/10.1017/aog.2016.29.

Song, Chunqiao, and Yongwei Sheng. 2016. Contrasting evolution patterns between glacier-fed and non-glacier-fed lakes in the Tanggula Mountains and climate cause analysis. Climatic Change 135(3): 493–507. doi: http://dx.doi.org/10.1007/s10584-015-1578-9.

Song, Chunqiao, et al. 2016. Glacial lake evolution in the southeastern Tibetan Plateau and the cause of rapid expansion of proglacial lakes linked to glacial-hydrogeomorphic processes. Journal of Hydrology 540: 504–514. doi: http://dx.doi.org/10.1016/j.jhydrol.2016.06.054.

Su, Yanjun, et al. 2016. Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. Remote Sensing of Environment 173: 187-199. doi: http://dx.doi.org/10.1016/j.rse.2015.12.002.

Su, Yanjun, Qin, and Qinghua Guo. 2016. Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery. International Journal of Digital Earth 10(3): 307-323. doi: http://dx.doi.org/10.1080/17538947.2016.1227380.

Takaku, Junichi, et al. 2016. Validation of 'AW3D' Global DSM Generated From ALOS Prism. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences III-4: 25-31.

Tang, H., et al. 2016. Characterizing leaf area index (LAI) and vertical foliage profile (VFP) over the United States. Biogeosciences 13(1): 239-252. doi: http://dx.doi.org/10.5194/bg-13-239-2016.

Tao, Shengli, et al. 2016. Global patterns and determinants of forest canopy height. Ecology 97(12): 3265–3270. doi: http://dx.doi.org/10.1002/ecy.1580.

Toyoda, Takahiro, et al. 2016. Data assimilation of sea ice concentration into a global ocean–sea ice model with corrections for atmospheric forcing and ocean temperature fields. Journal of Oceanography 72(2): 235-262. doi: http://dx.doi.org/10.1007/s10872-015-0326-0.

Treichler, D., and A. Kääb. 2016. ICESat laser altimetry over small mountain glaciers. Cryosphere 10(5): 2129-2146. doi: http://dx.doi.org/10.5194/tc-10-2129-2016.

Visakh, S., S. Muralikrishnan and M. Sreedhar. 2016. Improving the Elevation Accuracy of CARTOSAT -1 DEM. International Journal for Innovative Research in Science & Technology 2(8): 117-128.

Wang, Qiuyu, Shuang Yi, and Wenke Sun. 2016. The changing pattern of lake and its contribution to increased mass in the Tibetan Plateau derived from GRACE and ICESat data. Geophysical Journal International 207(1): 528-541. doi: http://dx.doi.org/10.1093/gji/ggw293.

Wang, Xianwei, et al. 2016. Grounding and calving cycle of Mertz Ice Tongue revealed by shallow Mertz Bank . The Cryosphere 10(5): 2043-2056. doi: http://dx.doi.org/10.5194/tc-10-2043-2016.

Wang, Xianwei, et al. 2016. Grounding and calving cycle of Mertz Ice Tongue revealed by shallow Mertz Bank. The Cryosphere 10(5): 2043–2056. doi: http://dx.doi.org/10.5194/tc-10-2043-2016.

Wang, Yuanyuan, et al. 2016. A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height. Remote Sensing of Environment 174: 24-43. doi: http://dx.doi.org/10.1016/j.rse.2015.12.005.

Wendleder, A., et al. 2016. A Method to Estimate Long-Wave Height Errors of SRTM C-Band DEM. IEEE Geoscience and Remote Sensing Letters 13(5): 696-700. doi: http://dx.doi.org/10.1109/LGRS.2016.2538822.

Xi, Xiaohuan, et al. 2016. Forest above Ground Biomass Inversion by Fusing GLAS with Optical Remote Sensing Data. International Journal of Geoinformation 5(4). Art. #45. doi: http://dx.doi.org/10.3390/ijgi5040045.

Xie, Huan, et al. 2016. A Least-Squares Adjusted Grounding Line for the Amery Ice Shelf Using ICESat and Landsat 8 OLI Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(11): 5113-5122. doi: http://dx.doi.org/10.1109/JSTARS.2016.2614758.

Xie, Huan, et al. 2016. Antarctic Ice Sheet Surface Mass Balance Estimates From 2003 to 2015 Using ICESat and CRYOSAT-2 Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic 41-B8. Prague: ISPRS, 549-553 . doi: http://dx.doi.org/10.5194/isprsarchives-XLI-B8-549-2016.

Xie, Huan, et al. 2016. A comparative study of changes in the Lambert Glacier/Amery Ice Shelf system, East Antarctica, during 2004–2008 using gravity and surface elevation observations. Journal of Glaciology 62(235): 888–904. doi: http://dx.doi.org/10.1017/jog.2016.76.

Xing, Yanqiu, et al. 2016. Estimation of Regional Forest AbovegroundBiomass Combining ICESat/GLAS Waveforms and HJ-1A/HSI Hyperspectral Imageries. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic 41(B7): 731-737. doi: http://dx.doi.org/10.5194/isprsarchives-XLI-B7-731-2016.

Yavaşlı, Doğukan Doğu. 2016. Estimation of above ground forest biomass at Muğla using ICESat/GLAS and Landsat data. Remote Sensing Applications: Society and Environment 4: 211–218. doi: http://dx.doi.org/10.1016/j.rsase.2016.11.004.

Yi, Donghui, et al. 2016. Antarctic sea-ice freeboard and estimated thickness from NASA's ICESat and IceBridge observations. Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 7682-7685. doi: http://dx.doi.org/10.1109/IGARSS.2016.7731003.

Yi, Shuang, et al. 2016. Changes in Mountain Glaciers, Lake Levels, and Snow Coverage in the Tianshan Monitored by GRACE, ICESat, Altimetry, and MODIS. Remote Sensing 8(10). Art. # 798. doi: http://dx.doi.org/10.3390/rs8100798.

Yue, Ma, et al. 2016. Waveform model of a laser altimeter for an elliptical Gaussian beam. Applied Optics 55(8): 1957-1965. doi: http://dx.doi.org/10.1364/AO.55.001957.

Zhang, Shuping, et al. 2016. Bathymetric survey of water reservoirs in north-eastern Brazil based on TanDEM-X satellite data. Science of The Total Environment 571: 575–593. doi: http://dx.doi.org/10.1016/j.scitotenv.2016.07.024.

Zhang, Tengyu, and Shuanggen Jin. 2016. Ice mass balance and GIA effects in tibet estimated from GRACE and ICESat measurements. Progress in Electromagnetic Research Symposium (PIERS), Shanghai: 4780-4783. doi: http://dx.doi.org/10.1109/PIERS.2016.7735748.

Zhou, Yuhong, et al. 2016. Curve matching approaches to waveform classification: a case study using ICESat data. GIScience & Remote Sensing 53(6): 739-758. doi: http://dx.doi.org/10.1080/15481603.2016.1232147.

Zou, Fang, and Shuanggen Jin. 2016. Estimations of glacier melting in Greenland from combined satellite gravimetry and icesat. Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International: 6185-6188. doi: http://dx.doi.org/10.1109/IGARSS.2016.7730616.

2015

Andersen, M. L., et al. 2015. Basin-scale partitioning of Greenland ice sheet mass balance components (2007–2011). Earth and Planetary Science Letters 409: 89-95. doi: http://dx.doi.org/10.1016/j.epsl.2014.10.015.

Arthern, Robert J., Richard C. A. Hindmarsh, and C. Rosie Williams. 2015. Flow speed within the Antarctic ice sheet and its controls inferred from satellite observations. Journal of Geophysical Research - Earth Surface 120(7): 1171-1188. doi: http://dx.doi.org/10.1002/2014JF003239.

Bao, Wei-jia, et al. 2015. Glacier changes during the past 40 years in the west Kunlun Shan. Journal of Mountain Science 12(2): 344-357. doi: http://dx.doi.org/10.1007/s11629-014-3220-0.

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