B13A:
Advances in Spaceborne Mapping and Monitoring of Aboveground Biomass II Posters

Submit an Abstract to this Session



Session ID#: 26828

Session Description:
High uncertainty remains in quantifying the magnitude and distribution of forest and woodland above ground biomass (AGB) stocks globally, providing a poor constraint on carbon flux modelling and limiting the accurate assessment of AGB changes resulting from disturbance and recovery. The anticipated launches of NASA and ESA missions (e.g. GEDI, NISAR, BIOMASS) designed for global AGB mapping will help reduce this uncertainty. The CEOS WGCV Land Product Validation subgroup has recently established a biomass focus area that will provide a forum from which to develop a protocol for the quantitative calibration and validation of AGB data products. This session will focus on recent advances in methods for AGB product generation, calibration and validation. We invite abstracts on SAR, lidar, multiangular and passive optical approaches to mapping AGB, singularly and in combination. Contributions on novel in situ validation methods (e.g. terrestrial laser scanning), scaling techniques, and intercomparison activities are particularly welcome.
Primary Convener:  John David Armston, University of Maryland College Park, Department of Geographical Sciences, College Park, MD, United States
Conveners:  Laura Duncanson1, Mathias Disney2 and Miguel O O Román1, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States(2)University College London, Department of Geography, London, United Kingdom
Chairs:  John David Armston, University of Maryland College Park, Department of Geographical Sciences, College Park, MD, United States and Laura Duncanson, NASA Goddard Space Flight Center, Greenbelt, MD, United States
OSPA Liaison:  John David Armston, University of Maryland College Park, Department of Geographical Sciences, College Park, MD, United States

Cross-Listed:
  • GC - Global Environmental Change
Index Terms:

0428 Carbon cycling [BIOGEOSCIENCES]
0439 Ecosystems, structure and dynamics [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1640 Remote sensing [GLOBAL CHANGE]

Abstracts Submitted to this Session:

Generation of large-scale forest height and disturbance maps through the fusion of NISAR and GEDI along with TanDEM-X/L (207541)
Yang Lei, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, Robert N Treuhaft, Jet Propulsion Laboratory, Pasadena, CA, United States, Paul Siqueira, University of Massachusetts Amherst, Electrical and Computer Engineering, Amherst, MA, United States, Nathan Torbick, Mitti Labs Limited, Mumbai, India, Richard Lucas, University of New South Wales, Sydney, Australia, Michael Maier Keller, EMBRAPA Brazilian Agricultural Research Corportation, Campinas, Brazil, Michael Schmidt, Remote Sensing Centre, Environment and Resource Sciences, Department of Science, Information Technology and Innovation, Brisbane, Australia, Mark J Ducey, University of New Hampshire, Department of Natural Resources, Durham, United States and William Salas, Applied Geosolutions, LLC, Durham, NH, United States
Forest biomass mapping from fusion of GEDI Lidar data and TanDEM-X InSAR data (292738)
Wenlu Qi1, Steven Hancock2, John David Armston3, Suzanne Marselis2 and Ralph Dubayah1, (1)University of Maryland College Park, College Park, MD, United States, (2)University of Maryland College Park, Geographical Sciences, College Park, MD, United States, (3)University of Maryland, College Park, United States
The GEDI Performance Tool (293929)
Steven Hancock1, John David Armston2, Hao Tang3, Paul L Patterson4, Sean P Healey4, Suzanne Marselis5, Laura Duncanson6, Michelle A Hofton7, James R Kellner8, Scott B Luthcke9, Xiaoli Sun6, James Bryan Blair10 and Ralph Dubayah1, (1)University of Maryland College Park, College Park, MD, United States, (2)University of Maryland, College Park, United States, (3)University of Maryland, College Park, MD, United States, (4)Rocky Mountain Research Station, Ogden, United States, (5)University of Maryland College Park, Geographical Sciences, College Park, MD, United States, (6)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (7)Univ Maryland, College Park, MD, United States, (8)Brown University, Providence, RI, United States, (9)NASA Goddard Space Flight Center, Geodesy and Geophysics Laboratory, Greenbelt, MD, United States, (10)NASA Goddard Space Flight Center, Code 61A, Greenbelt, MD, United States
Statistical Properties for Hybrid Estimators of Biomass that use GEDI Waveform Data (262054)
Paul L Patterson1, Sean P Healey2, Göran Ståhl3, Svetlana Saarela4, Sören Holm4, Ralph Dubayah5 and Steven Hancock5, (1)US Forest Service Fort Collins, Fort Collins, CO, United States, (2)Rocky Mountain Research Station, Ogden, United States, (3)Swedish University of Agricultural Sciences, Department of Forest Resource Management, Umeå, Sweden, (4)Swedish University of Agricultural Sciences, Department of Forest Resource Management, Umea, Sweden, (5)University of Maryland College Park, Geographical Sciences, College Park, MD, United States
How Much Can Remotely-Sensed Natural Resource Inventories Benefit from Finer Spatial Resolutions? (264233)
Zhengyang Hou, University of Nevada Reno, Reno, NV, United States, Qing Xu, Chinese Academy of Forestry, Research Institute of Forest Ecology, Environment and Protection, Beijing, China, Ronald E McRoberts, USDA Forest Service, St. Paul, MN, United States, Göran Ståhl, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Umeå, Sweden and Jonathan A Greenberg, University of Nevada Reno, Natural Resources and Environmental Science, Reno, United States
Identifying forest patterns from space to explore dynamics across the circumpolar boreal (261713)
Paul M Montesano1, Christopher S R Neigh2, Min Feng3, Saurabh Channan4, Joseph O Sexton5, William Wagner1, Margaret Wooten6, Benjamin Poulter7 and Libo Wang8, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD, United States, (3)National Tibetan Plateau Data Center (TPDC), State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China, (4)University of Maryland College Park, Global Land Cover Facility, College Park, MD, United States, (5)Global Land Cover Facility, College Park, MD, United States, (6)NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, United States, (7)Spark Climate Solutions, Greenbelt, United States, (8)Environment and Climate Change Canada, Climate Research Division, Toronto, ON, Canada
25m-resolution Global Mosaic and Forest/Non-Forest map using PALSAR-2 data set (271119)
Takuya Itoh1, Masanobu Shimada2, Takeshi Motooka3, Masato Hayashi4, Takeo Tadono4, Risako DAN5, Osamu Isoguchi1 and Tsutomu Yamanokuchi1, (1)RESTEC Remote Sensing Technology Center of Japan, Tokyo, Japan, (2)Tokyo Denki University, Hatoyama, Japan, (3)JAXA Japan Aerospace Exploration Agency, Tsukuba, Japan, (4)JAXA, Tsukuba, Japan, (5)RESTEC Remote Sensing Technology Center of Japan, Tsukuba city, Japan
Bridging scale gaps between regional maps of forest aboveground biomass and field sampling plots using TanDEM-X data (298085)
Wenjian Ni, RADI Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China, Zhiyu Zhang, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China and Guoqing Sun, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Mapping of past stand-level forest disturbances and estimation of time since disturbance using simulated spaceborne LiDAR data (279437)
Nuria Sanchez Lopez, University of Idaho, Moscow, ID, United States, Andrew T Hudak, USDA Forest Service, Rocky Mountain Research Station, Moscow, United States and Luigi Boschetti, University of Idaho, College of Natural Resources, Moscow, United States
Analyzing spatial and temporal trends in Aboveground Biomass within the Acadian New England Forests using the complete Landsat Archive (229145)
John Burns Kilbride1, Shawn Fraver2, Elias Ayrey1, Aaron Weiskittel3, Justin Braaten4, Joseph Michael Hughes5 and Daniel J Hayes6, (1)University of Maine, Orono, ME, United States, (2)University of Maine, School of Forest Resources, Orono, United States, (3)University of Maine, Center for Research on Sustainable Forests, Orono, United States, (4)Oregon State University, College of Forestry, Corvallis, United States, (5)Oregon State University, College of Earth Ocean and Atmospheric Sciences, Corvallis, OR, United States, (6)Battelle Oak Ridge, Oak Ridge, TN, United States
Does Sentinel multi sensor data offer synergy in Improving Accuracy of Aboveground Biomass Estimate of Dense Tropical Forest? - Utility of Decision Tree Based Machine Learning Algorithms (266408)
Sujit Madhab Ghosh, Indian Institute of Technology Kharagpur, Kharagpur, India and Mukunda Dev Behera, Indian Institute of Technology Kharagpur, Centre for Ocean, River, Atmosphere and Land Sciences, Kharagpur, India
Spaceborne SAR Data for Aboveground-Biomass Retrieval of Indian Tropical Forests (266787)
Unmesh Khati1, Konica Bhandari2 and Mohamed Musthafa2, (1)Indian Institute of Technology Bombay, Mumbai, India, (2)Indian Institute of Technology Bombay, Center of Studies in Resources Engineering, Mumbai, India
Phytomass & Carbon estimation of Tree outside Forests, their role and effect in maintaing microclimate of local environment: A Remote Sensing Approach in Gwalior and Sheopur District, Madhya Pradesh (274678)
Swati Uniyal, Andhra University, Visakhapatnam, India, Sarnam Singh, Indian Institute of Remote Sensing, Dehradun, India and Sitiraju Srinivasa Rao, RRSC- W National Remote Sensing center, ISRO, Jodhpur, India

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