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Understanding how hillslope topography modulates ecosystem dynamics across topoclimatic gradients is critical for predicting future climate change impacts on vegetation function. We examined the influence of hillslope topography on ecosystem productivity, structure, and photosynthetic activity across a range of water and energy availability using three independent methods in a forested watershed (Montana, USA): 308 tree cores; light detection and ranging quantification of stem density, basal area, foliar biomass, and total biomass; and the enhanced vegetation index (EVI; 1984–2012). Multiple linear regression analysis across three conifer species revealed significant increases in measured basal area increment growth rates (from 56 to 2,058 mm2/yr) with increasing values of the topographic wetness index and decreases in the climatic water deficit. At the watershed scale, we observed strong gradients in total biomass (e.g., 52 to 75 Mg/ha), which increased from ridgelines to convergent hollows. The most predominant topographic organization of forest biomass occurred along locations of climatically driven water limitations. Similarly, an analysis of growing season EVI indicated enhanced photosynthetic activity and a prolonged growing season in convergent hillslope positions. Collectively, these analyses confirm that within water‐limited landscapes, meter‐scale differences in topographic position can mediate the effects of the local energy balance and contribute to large differences in local hydrometeorological processes that are a necessary consideration for quantifying spatial patterns of ecosystem productivity. Further, they suggest that local topography and its topology with regional climate may become increasingly important for understanding spatial patterns of ecosystem productivity, mortality, and resilience as regional climates become more arid.

It has been suggested that hillslope topography can produce hydrologic refugia, sites where ecosystem productivity is relatively insensitive to climate variation. However, the ecological impacts and spatial distribution of these sites are poorly resolved across gradients in climate. We quantified the response of ecosystem net primary productivity to changes in the annual climatic water balance for 30 years using pixel-specific linear regression (30-m resolution) across the western United States. The standardized slopes of these models represent ecosystem climate sensitivity and provide a means to identify drought-resistant ecosystems. Productive and resistant ecosystems were most frequent in convergent hillslope positions, especially in semiarid climates. Ecosystems in divergent positions were moderately resistant to climate variability, but less productive relative to convergent positions. This topographic effect was significantly dampened in hygric and xeric climates. In aggregate, spatial patterns of ecosystem sensitivity can be implemented for regional planning to maximize conservation in landscapes more resistant to perturbations. Plain Language Summary:It is well known that gradients in elevation and aspect can have a significant influence on the degree of water and energy available for plant growth and the sensitivity of ecosystems to wet or dry time periods. Little work has examined how hillslope topography and downslope movement of water to zones of convergent terrain can impact plant available water and vegetation growth. We quantified ecosystem response to the climatic water balance (ecosystem sensitivity) across a 30-year record and at a 30-m resolution across the western United States. Our results show that vegetation in zones of hillslope convergence, where moisture from upslope tends to accumulate, is less sensitive to droughts, especially in semiarid settings. Divergent hillslope positions were moderately sensitive to climate and less productive relative to convergent positions. Ecosystem response to topography was dampened in especially wet or dry climates due to significant moisture surplus or moisture deficit, respectively. These distributed measurements of ecosystem sensitivity are important considerations when describing local ecosystem-climate relationships and for identifying management priorities across landscapes. Zones of resistant vegetation are more likely to persist through future droughts, influencing the greater ecosystem's response to climate change.

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Terrestrial laser scanning (TLS) was used to collect spatially continuous measurements of fuelbed characteristics across the plots and burn blocks of the 2012 RxCADRE experiments in Florida. Fuelbeds were scanned obliquely from plot/block edges at a height of 20 m above ground. Pre-fire blocks were scanned from six perspectives and four perspectives for post-fire at ~2 cm nominal spot spacing. After processing, fuel height models were developed at one meter spatial resolution in burn blocks and compared with field measurements of height. Spatial bias is also examined. The resultant fuel height data correspond closely with field measurements of height and exhibit low spatial bias. They show that field measurements of fuel height from field plots are not representative of the burn blocks as a whole. A translation of fuel height distributions to specific fuel attributes will be necessary to maximise the utility of the data for fire modelling.

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Abstract:Requirements for describing coniferous forests are changing in response to wildfire concerns, bio-energy needs, and climate change interests. At the same time, technology advancements are transforming how forest properties can be measured. Terrestrial Laser Scanning (TLS) is yielding promising results for measuring tree biomass parameters that, historically, have required costly destructive sampling and resulted in small sample sizes. Here we investigate whether TLS intensity data can be used to distinguish foliage and small branches (≤0.635 cm diameter; coincident with the one-hour timelag fuel size class) from larger branchwood (>0.635 cm) in Douglas-fir (Pseudotsuga menziesii) branch specimens. We also consider the use of laser density for predicting biomass by size class. Measurements are addressed across multiple ranges and scan angles. Results show TLS capable of distinguishing fine fuels from branches at a threshold of one standard deviation above mean intensity. Additionally, the relationship between return density and biomass is linear by fuel type for fine fuels (r 2 = 0.898; SE 22.7%) and branchwood (r 2 = 0.937; SE 28.9%), as well as for total mass (r 2 = 0.940; SE 25.5%).Intensity decays predictably as scan distances increase; however, the range-intensity relationship is best described by an exponential model rather than 1/d 2 . Scan angle appears to have no systematic effect on fine fuel discrimination, while some differences are observed in density-mass relationships with changing angles due to shadowing.

The success of a local maximum (LM) tree detection algorithm for detecting individual trees from lidar data depends on stand conditions that are often highly variable. A laser height variance and percent canopy cover (PCC) classification is used to segment the landscape by stand condition prior to stem detection. We test the performance of the LM algorithm using canopy height model (CHM) smoothing decisions and crown width estimation for each stand condition ranging from open savannah to multi-strata stands. Results show that CHM smoothing improves stem predictions for low density stands and no CHM smoothing better detects stems in dense even-aged stands, specifically dominant and co-dominant trees (R 2 ϭ 0.61, RMSE ϭ 20.91 stems with smoothing; R 2 ϭ 0.85, RMSE ϭ 46.02 stems with no-smoothing; combined smoothed CHM for low density and unsmoothed CHM for high density stands R 2 ϭ 0.88, RMSE ϭ 28.59 stems). At a threshold of approximately 2,200 stems ha Ϫ1 , stem detection accuracy is no longer obtainable in any stand condition.