Using multi-source data from lidar, radar, imaging spectroscopy, and national forest inventories to simulate forest carbon fluxes

Antonarakis, A S, Siquiera, P and Munger, J W (2017) Using multi-source data from lidar, radar, imaging spectroscopy, and national forest inventories to simulate forest carbon fluxes. International Journal of Remote Sensing, 38 (19). pp. 5464-5486. ISSN 0143-1161

[img] PDF - Published Version
Restricted to SRO admin only

Download (2MB)
[img] PDF - Accepted Version
Restricted to SRO admin only until 19 June 2018.

Download (1MB)

Abstract

Terrestrial biosphere carbon dynamics are the most uncertain elements of the global carbon budget. Carbon stocks estimated using spatially extensive remote sensing are crucial in reducing this uncertainty, and using these stocks as initial conditions to biosphere models can improve carbon flux predictions beyond the site level. Yet remote-sensing data are not always consistently available for large regions, so methods assessing carbon uncertainty using data sources in one location may not be transferable to another. This study assesses the use of multiple-source data from lidar, radar, imaging spectroscopy, and national forest inventories to derive forest structure and composition necessary to initialise the Ecosystem Demography model (ED2), and hence evaluate short-term carbon flux uncertainty over Harvard Forest, Massachusetts. ED2 was initialized using forest structure and composition derived from lidar and national forest inventories, radar and national forest inventories, lidar and imaging spectroscopy, and radar and imaging spectroscopy resulting in net ecosystem productivity uncertainty of 26.3%, 41.9%, 19.6%, and 20.2%, respectively, compared to ground-based forest inventory initializations. This study uniquely offers a multitude of methods to estimate forest ecosystem state, with resulting carbon uncertainties, transferable to regions with potentially different data availability. Furthermore, in preparation for satellite radar, lidar, and imaging spectrometer, this study highlights the importance of combining techniques deriving forest structure and composition at different scales, binding regional to potentially global carbon-fluxes with remote sensing, reducing this uncertainty source in global climate models.

Item Type: Article
Schools and Departments: School of Global Studies > Geography
Research Centres and Groups: climate@sussex
Subjects: G Geography. Anthropology. Recreation > GB Physical geography
Q Science > QK Botany > QK0900 Plant ecology
Depositing User: Alexander Antonarakis
Date Deposited: 19 Jun 2017 09:42
Last Modified: 05 Aug 2017 12:37
URI: http://sro.sussex.ac.uk/id/eprint/68703

View download statistics for this item

📧 Request an update