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Removal of nonnative riparian trees is accelerating to conserve water and improve habitat for native species. Widespread control of dominant species, however, can lead to unintended erosion. Helicopter herbicide application in 2003 along a 12-km reach of the Rio Puerco, New Mexico, eliminated the target invasive species saltcedar (Tamarix spp.), which dominated the floodplain, as well as the native species sandbar willow (Salix exigua Nuttall), which occurred as a fringe along the channel. Herbicide application initiated a natural experiment testing the importance of riparian vegetation for bank stability along this data-rich river. A flood three years later eroded about 680,000 m(3) of sediment, increasing mean...
Spatially explicit forest carbon (C) monitoring aids conservation and climate change mitigation efforts, yet few approaches have been developed specifically for the highly heterogeneous landscapes of oceanic island chains that continue to undergo rapid and extensive forest C change. We developed an approach for rapid mapping of aboveground C density (ACD; units = Mg or metric tons C ha−1) on islands at a spatial resolution of 30 m (0.09 ha) using a combination of cost-effective airborne LiDAR data and full-coverage satellite data. We used the approach to map forest ACD across the main Hawaiian Islands, comparing C stocks within and among islands, in protected and unprotected areas, and among forests dominated by...
Peatlands are a major reservoir of global soil carbon, yet account for just 3% of global land cover. Human impacts like draining can hinder the ability of peatlands to sequester carbon and expose their soils to fire under dry conditions. Estimating soil carbon loss from peat fires can be challenging due to uncertainty about pre-fire surface elevations. This study uses multi-temporal LiDAR to obtain pre- and post-fire elevations and estimate soil carbon loss caused by the 2011 Lateral West fire in the Great Dismal Swamp National Wildlife Refuge, VA, USA. We also determine how LiDAR elevation error affects uncertainty in our carbon loss estimate by randomly perturbing the LiDAR point elevations and recalculating elevation...
This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory variables performed well with lidar models having slightly higher explained variance and lower root mean square error. Adjusted R2 values were 0.93 and 0.93 for mean height, 0.74 and 0.73 for leaf area index, and 0.93 and 0.85 for all carbon in live biomass for the lidar and CCA explanatory regression models, respectively. The CCA results indicate...