Fusion of optical and SAR data for forestry applications in the Sierra Nevada of California

Ecologists and forest managers commonly emphasize the description of forest structural features because these elements often serve as indicators of organisms and surrogates for processes that may be difficult to observe or measure directly, such as wildlife habitat suitability and the dynamics of forest ecosystems. As used here, the term structure refers to the numbers, sizes, and shapes of the vegetative components in a forest ecosystem and their spatial distribution. Key attributes of forest structure include above-ground biomass, canopy cover, tree height, large tree density, and three-dimensional structural complexity. Remote sensing is a particularly attractive alternative to ground-based measurements because data can be acquired repeatedly and across broad geographic areas that might otherwise be inaccessible. Mapped estimates of forest structural attributes are therefore considered critical to ongoing monitoring efforts requiring reliable inventories of forest resources and accurate assessments of species status and trend. Two pilot study areas were established in the Sierra Nevada mountain range of California for the purposes of characterizing the three-dimensional structure of selected Sierran forest vegetation types. Field measurements are being used to calibrate and validate estimates of forest structural attributes derived using remote-sensing techniques. Study area locations were selected to represent the pronounced elevational (hi/low) and latitudinal (north/south) gradients that distinguish the Sierra Nevada range. One study area, representing the southern Sierra, was established on the Sierra National Forest and includes the 60,000-ha King s River Sustainable Forest Ecosystem Project and the 1300-ha Teakettle Creek Experimental Forest. The second study area, representing the northern Sierra, was located on the Plumas National Forest. Within each study area, a stratified-random sampling scheme was used. A 3% sample resulted in a total of 500 1-ha sample plots.