The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity—an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research.

Topoclimatic diversity within forest landscapes can underlie variation in water availability, which may correspond to patterns in habitat suitability of tree species with differing hydrologic niches. However, the trade‐off between the collection of data at a fine grain size over large spatial extents has limited comprehensive analyses of landscape scale variation in habitat suitability. We present a fine scale analysis of the roles of topographic gradients in moisture availability, soil water storage and fog frequency in the spatial pattern of habitat suitability for coast redwood Sequoia sempervirens , at 10 m resolution across 34 800 ha and three landscapes spanning approximately one‐third of redwood's latitudinal range. A new 10 m resolution presence‐absence map derived from airborne imaging spectroscopy was used to characterize current redwood distributions in three landscapes. Access to belowground moisture was assessed through four measures calculated from a high‐resolution LiDAR digital elevation model, soil water storage was mapped from the USDA Soil Survey Geographic Database, and fog frequency was calculated from MODIS cloud cover data. Access to belowground moisture and fog predicted variation in suitability as redwood habitat from 22–75% and predicted redwood presence and absence at 10 m resolution with 63–74% accuracy. Across all three landscapes, redwood density consistently decreased with increasing interpolated height above a stream (IHAS), but the role of other predictors varied among the sites. Our results challenge previous assumptions that redwood habitat suitability within much coarser resolutions (800 m × 800 m) can be described by a single value. The elevated habitat suitability of sites close to streams suggests that these sites should be evaluated for their potential to become microrefugia within landscapes that may become climatically‐unsuitable for redwoods at a regional scale.

High-resolution maps of redwood distributions could enable strategic land management to satisfy diverse conservation goals, but the currently-available maps of redwood distributions are low in spatial resolution and biotic detail. Classification of airborne imaging spectroscopy data provides a potential avenue for mapping redwoods over large areas and with high confidence. We used airborne imaging spectroscopy data collected over three redwood forests by the Carnegie Airborne Observatory, in combination with field training data and application of a gradient boosted regression tree (GBRT) machine learning algorithm, to map the distribution of redwoods at 2-m spatial resolution. Training data collected from the three sites showed that redwoods have spectral signatures distinct from the other common tree species found in redwood forests. We optimized a gradient boosted regression model for high performance and computational efficiency, and the resulting model was demonstrably accurate (81–98% true positive rate and 90–98% overall accuracy) in mapping redwoods in each of the study sites. The resulting maps showed marked variation in redwood abundance (0–70%) within a 1 square kilometer aggregation block, which match the spatial resolution of currently-available redwood distribution maps. Our resulting high-resolution mapping approach will facilitate improved research, conservation, and management of redwood trees in California.