Improving efficiency in broad-scale vegetation inventories:

Examples from the Nevada photo-based inventory pilot

 

 

Gretchen Moisen[1], Tracey Frescino1, Val Nelson1, Kevin Megown[2], Mark Finco2, Paul Patterson1

 

 

The U.S. Forest Service Forest Inventory and Analysis Program (FIA) conducts and continuously updates a comprehensive inventory of the forests throughout the U.S. The complex nature of this type of broad-scale, strategic-level inventory demands constant evolution and evaluation of methods to get the best information possible while continuously increasing efficiency. The state of Nevada poses some interesting challenges for FIA: it is not yet funded for annual inventory; it has the most incomplete and outdated periodic data in the Interior West; it is comprised of predominantly non-forested federal lands; and the small proportion of forest land is dominated by woodland tree species. Consequently, it offers a good test bed for alternative methodologies to improve precision in estimates of forest parameters, reduce field data collection costs, address the potential of strategic-level inventory on lands not traditional sampled by FIA such as rangelands and riparian areas. In 2004, the ÒNevada Photo-based Inventory PilotÓ (NPIP) was launched involving the acquisition and processing of large-scale real time GPS-controlled aerial photography (LSAP) throughout the State of Nevada over two field seasons. The over-arching goals of this pilot are to exceeding information requirements, step-up inventory timeline, and reduce inventory costs. In this paper, we describe the photo-interpretation process, outline the estimation strategies, and report initial results for White Pine county. On both forest and nonforest lands, we illustrate level of details that can be obtained from LSAP, repeatability between photo-interpreters, and consistency with field calls. For forested lands, we compare photo- and field-based estimates of area by forest type and percent cover by species, and present a cost-analysis for use of LSAP in an annual inventory system.

 



[1] USFS - Rocky Mountain Research Station, Forest Inventory and Analysis, Ogden, UT

 

[2] USFS - Remote Sensing Applications Center, Salt Lake City, UT