The challenges inherent in field validation data, and real-world light detection and ranging (lidar) collections make it difficult to assess the best algorithms for using lidar to characterize forest stand volume. Here, we demonstrate the use of synthetic forest stands and simulated terrestrial laser scanning (TLS) for the purpose of evaluating which machine learning algorithms, scanning configurations, and feature spaces can best characterize forest stand volume. The random forest (RF) and support vector machine (SVM) algorithms generally outperformed k-nearest neighbor (kNN) for estimating plot-level vegetation volume regardless of the input feature space or number of scans. Also, the measures designed to characterize occlusion using spherical voxels generally provided higher predictive performance than measures that characterized the vertical distribution of returns using summary statistics by height bins. Given the difficulty of collecting a large number of scans to train models, and of collecting accurate and consistent field validation data, we argue that synthetic data offer an important means to parameterize models and determine appropriate sampling strategies.
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Summary Decades of atmospheric nitrogen (N) deposition in the northeastern USA have enhanced this globally important forest carbon (C) sink by relieving N limitation. While many N fertilization experiments found increased forest C storage, the mechanisms driving this response at the ecosystem scale remain uncertain.
Following the optimal allocation theory, augmented N availability may reduce belowground C investment by trees to roots and soil symbionts. To test this prediction and its implications on soil biogeochemistry, we constructed C and N budgets for a long‐term, whole‐watershed N fertilization study at the Fernow Experimental Forest, WV, USA.
Nitrogen fertilization increased C storage by shifting C partitioning away from belowground components and towards aboveground woody biomass production. Fertilization also reduced the C cost of N acquisition, allowing for greater C sequestration in vegetation. Despite equal fine litter inputs, the C and N stocks and C : N ratio of the upper mineral soil were greater in the fertilized watershed, likely due to reduced decomposition of plant litter.
By combining aboveground and belowground data at the watershed scale, this study demonstrates how plant C allocation responses to N additions may result in greater C storage in both vegetation and soil.
