Next-generation forest change mapping across the United States: the landscape change monitoring system (LCMS)

Sean P. Healey; Warren B. Cohen; Yang Zhiqiang; Ken Brewer; Evan Brooks; Noel Gorelick; Mathew Gregory; Alexander Hernandez; Chengquan Huang; Joseph Hughes; Robert Kennedy; Thomas Loveland; Kevin Megown; Gretchen Moisen; Todd Schroeder; Brian Schwind; Stephen Stehman; Daniel Steinwand; James Vogelmann; Curtis Woodcock; Limin Yang; Zhe Zhu

Next-generation forest change mapping across the United States: the landscape change monitoring system (LCMS)

Informally Refereed

Authors:	Sean P. Healey, Warren B. Cohen, Yang Zhiqiang, Ken Brewer, Evan Brooks, Noel Gorelick, Mathew Gregory, Alexander Hernandez, Chengquan Huang, Joseph Hughes, Robert Kennedy, Thomas Loveland, Kevin Megown, Gretchen Moisen, Todd Schroeder, Brian Schwind, Stephen Stehman, Daniel Steinwand, James Vogelmann, Curtis Woodcock, Limin Yang, Zhe Zhu
Year:	2015
Type:	General Technical Report
Station:	Rocky Mountain Research Station
Source:	In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. p. 217.

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Abstract

Forest change information is critical in forest planning, ecosystem modeling, and in updating forest condition maps. The Landsat satellite platform has provided consistent observations of the world’s ecosystems since 1972. A number of innovative change detection algorithms have been developed to use the Landsat archive to identify and characterize forest change. The inter-agency Landscape Change Monitoring System (LCMS) has been launched to engage these cutting edge methodologies in a national-scale, sustained change monitoring operation. A Science Team supporting LCMS has evaluated the relative strengths of several leading algorithms in identifying different types of forest change across a variety of ecosystems. Additionally, a machine-learning approach has been developed that uses an ensemble of algorithm outputs to predict a surface which best matches independently collected reference data. This ensemble technique integrates the strengths of each individual algorithm in different situations, and has been shown to reduce overall error in LCMS trials. The LCMS Science Team has also, in collaboration with Google, overcome significant data processing hurdles associated with analyzing tens of thousands of large images. Following Science Team recommendations, LCMS is quickly moving toward production and maintenance of validated, nationally consistent maps of the causes and timing of historical forest change.

Parent Publication

Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015

Citation

Healey, Sean P.; Cohen, Warren B.; Zhiqiang, Yang; Brewer, Ken; Brooks, Evan; Gorelick, Noel; Gregory, Mathew; Hernandez, Alexander; Huang, Chengquan; Hughes, Joseph; Kennedy, Robert; Loveland, Thomas; Megown, Kevin; Moisen, Gretchen; Schroeder, Todd; Schwind, Brian; Stehman, Stephen; Steinwand, Daniel; Vogelmann, James; Woodcock, Curtis; Yang, Limin; Zhu, Zhe. 2015. Next-generation forest change mapping across the United States: the landscape change monitoring system (LCMS). In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8 10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. p. 217.