Remote sensing for mapping and modeling of land-based carbon flux and storage

Nancy H.F. French, Michigan Technological University
Laura L. Bourgeau-Chavez, Michigan Technological University
Michael J. Falkowski, Michigan Technological University
Scott J. Goetz, Woods Hole Research Center
Liza K. Jenkins, Michigan Technological University
Philip Camill, Bowdoin College
Collin S. Roesler, Bowdoin College
Daniel G. Brown, University of Michigan, Ann Arbor


An essential aspect of carbon (C) accounting is the development of methods and technologies for measurement and monitoring of C pools and fluxes. Forest and agricultural systems are key to the C cycle, as they hold and rapidly exchange large amounts of C, and human-influenced dynamics of C in these systems are very large. Wetlands, streams, and rivers are important reservoirs and exchange points for C, with C in land and hydrologic systems vulnerable to land-use impacts and other natural disturbance forces. In the context of climate change, the sizes of C pools and magnitudes of C fluxes (see Chapter 2) need to be both well understood for modeling purposes and accurately monitored to quantify and attribute changes driven by land-change processes and confounded by climate-change forces. Direct-measurement methods for C accounting, such as a ground-based inventories, can be inappropriate for covering large landscapes to document extensive C pools or for repeating measurements needed to adequately account for C dynamics. However, if properly deployed, remote sensing systems can be used to provide the spatially synoptic and temporally frequent coverage needed to document land conditions and changes over time (Cohen and Goward 2004; Houghton and Goetz 2008). Remote sensing tools and techniques have developed since the first airborne sensors (photographic cameras) were deployed in the early 1900s. They have progressed from simple passive recording devices to advanced passive and active sensing systems operating from airborne and spaceborne platforms. Remote sensing science includes the data collection technologies and data analysis techniques developed to use remotely sensed data within the framework of spatial data analyses.