Springs support some of the most diverse and unique ecosystems on Earth, but their stewardship has been hindered by the lack of knowledge of the distribution and density of springs across landscapes. Death Valley National Park (DEVA) and the State of Arizona in the USA are 2 landscapes for which significant knowledge exists about the distribution and density of springs. We used data on springs in DEVA to test the application of accumulation curves for estimating spring density. We used a spring-specific database in Arizona as an example of how to compile geospatial information for a large landscape. In both landscapes, springs are nonrandomly distributed because they emerge in topographically and geologically complex terrain and in clusters of multiple sources. Thus, estimates of their density depend on the spatial scale of inquiry and the extent to which sources are considered independent. For example, based on the current inventory, density in DEVA is estimated to be 0.033 to 0.074 springs/km² depending on whether springs are defined as individual orifices or as complexes (groups of related spring orifices). The best data for springs as individual orifices yield an estimated 0.035 springs/km² in Arizona. These densities are based on current data sets, and an unknown number of springs remain unmapped in both landscapes. To predict the total number of springs in DEVA, we used a modified density accumulation curve, involving the number of springs detected in surveys over the past century. The analysis indicated that undocumented springs may exist across the landscape. Knowledge of the distribution and density of the springs can help land and resource managers develop unbiased prioritizations of spring ecosystems for stewardship actions. Management actions could benefit further from an understanding of the emergence environment of a complex of springs, instead of each emergence point of a spring in a complex.

Terrestrial laser scanners (TLSs) provide a tool to assess and monitor forest structure across forest landscapes. We present TLS methods, suggestions, and mapped guidelines for planning TLS acquisitions at varying scales and forest densities. We examined rates of point‐density decline with distance from two TLS that acquire data at relatively high and low point density and found that the rates were nearly identical between scanners (pvalue <0.01), suggesting that our findings are applicable to a range of TLS types. Using unique, TLS‐adapted processing methods, we determined the relative accuracy of TLS‐derived plot‐scale estimates of tree height, diameter‐at‐breast‐height, height‐to‐canopy, tree counts, as well as treatment‐scale tree density and patch metrics, using both high point density and low point density TLS among thinned and nonthinned forest treatments. The high‐density TLS consistently provides more accurate estimates of plot‐level metrics (R² = 0.46 to 0.87) than the low‐density TLS (R² = −0.14 to 0.53). At treatment scales, tree density estimates are similar among scanners (R² = 0.95 vs. 0.71), as are canopy cover and patch metrics. We develop and present the normalized density‐distance index (NDDI), which can account for up to 59% of the variance in estimate error and can be used to guide TLS‐data acquisition plans. This index indicates whether a given location has generally higher point density (higher NDDI) relative to the distance from the scanner and can be used as a proxy for uncertainty. Using NDDI as a guide for fair comparison between scanners, both plot‐ and treatment‐scale estimates improved.