B045:
Linking Individual-Based Models with Remote Sensing to Enhance Large-Scale Prediction of Forest Productivity, Characteristics, and Response to Environmental Change





Session ID#: 26490

Session Description:
Individual-based gap models (IBGM)s have increased our understanding of forest (and other) ecosystem dynamics at landscape and larger scales since the 1970s. IBGMs allow an up-scaling of directly measured plot data to obtain stand- and landscape-level predictions, including: forest succession, structural dynamics, and productivity. As has been verified by paleo-reconstructions of forest composition and in simulation of forest responses to environmental gradients, these models can predict vegetation response to environmental changes. Application of IBGMs at continental and global scales is now attainable with recent advances in supercomputing. Concurrently, advances in high-resolution remote sensing products allow for tree-level measurements of forest structure and biogeochemistry. This session will present studies that inform or validate IBGMs or similar high-resolution forest models with remote sensing measurements, topics significant to both the Biogeosciences and the Global Environmental Change sections of AGU.
Primary Convener:  Amanda Hildt Armstrong, NASA Goddard Space Flight Center, Greenbelt, MD, United States; University of Virginia, Charlottesville, VA, United States
Conveners:  Adrianna Foster, University of Virginia, Charlottesville, VA, United States and Herman Henry Shugart Jr, University of Virginia Main Campus, Charlottesville, VA, United States

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

0439 Ecosystems, structure and dynamics [BIOGEOSCIENCES]
0466 Modeling [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1632 Land cover change [GLOBAL CHANGE]

Abstracts Submitted to this Session:

Xiaodong Yan, Beijing Normal University, Beijing, China; Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing, China
FangJing Jing Fang, Beijing Normal University, Beijing, China
Jingjing Peng, University of Maryland College Park, College Park, MD, United States, Wenjie Fan, Peking University, Beijing, China and Lizhao Wang, University of Maryland, College park, MD, United States
Shengbiao Wu, RADI Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China and Jianguang Wen, CAS Chinese Academy of Sciences, Institute of remote sensing and digital earth, Beijng, China
Qiao Song1, Xiuhong Li2 and Qiang Liu2, (1)Beijing Normal University, College of Global Change and Earth System Science, Beijing, China, (2)Beijing Normal University, Beijing, China
Alexander S Antonarakis, University of Sussex, Brighton, BN1, United Kingdom, Stacy Bogan, Harvard University, Center for Geographic Analysis, Cambridge, MA, United States and Paul R Moorcroft, Harvard Univ, Cambridge, MA, United States
Jon Ranson1, Guoqing Sun1, Amanda Hildt Armstrong1, Batuhan Osmanoglu1, Rico Fischer2 and Andreas Huth2, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Helmholtz Centre for Environmental Research–UFZ, Department of Ecological Modelling, Leipzig, Germany
Emanuelle A Feliciano1, Batuhan Osmanoglu1, Amanda Hildt Armstrong1, Guoqing Sun1, Paul Montesano2 and Kenneth Ranson1, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Biospheric Sciences Laboratory, Code 618, NASA Goddard Space Flight Center, Greenbelt, MD, United States

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