Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery.

Applications of LiDAR remote sensing are exploding, while moving from the research to the operational realm. Increasingly, natural resource managers are recognizing the tremendous utility of LiDAR-derived information to make improved decisions. This review provides a cross-section of studies, many recent, that demonstrate the relevance of LiDAR across a suite of terrestrial natural resource disciplines including forestry, fire and fuels, ecology, wildlife, geology, geomorphology, and surface hydrology. We anticipate that interest in and reliance upon LiDAR for natural resource management, both alone and in concert with other remote sensing data, will continue to rapidly expand for the foreseeable future.

Recent years have seen the progression of light detection and ranging (lidar) from the realm of research to operational use in natural resource management. Numerous government agencies, private industries, and public/private stakeholder consortiums are planning or have recently acquired large-scale acquisitions, and a national U.S. lidar acquisition is likely before 2020. Before it is feasible for land managers to integrate lidar into decision making, resource assessment, or monitoring across the gambit of natural resource applications, consistent standards in project planning, data processing, and user-driven products are required. This paper introduces principal lidar acquisition parameters, and makes recommendations for project planning, processing, and product standards to better serve natural resource managers across multiple disciplines.

Reactive nitrogen (N_r) within smoke plumes plays important roles in the production of ozone, the formation of secondary aerosols, and deposition of fixed N to ecosystems. The Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE‐CAN) field campaign sampled smoke from 23 wildfires throughout the western U.S. during summer 2018 using the NSF/NCAR C‐130 research aircraft. We empirically estimate N_r normalized excess mixing ratios and emission factors from fires sampled within 80 min of estimated emission and explore variability in the dominant forms of N_r between these fires. We find that reduced N compounds comprise a majority (39%–80%; median = 66%) of total measured reactive nitrogen (ΣN_r) emissions. The smoke plumes sampled during WE‐CAN feature rapid chemical transformations after emission. As a result, within minutes after emission total measured oxidized nitrogen (ΣNO_y) and measured total ΣNH_x (NH₃ + pNH₄) are more robustly correlated with modified combustion efficiency (MCE) than NO_x and NH₃ by themselves. The ratio of ΣNH_x/ΣNO_y displays a negative relationship with MCE, consistent with previous studies. A positive relationship with total measured ΣN_r suggests that both burn conditions and fuel N content/volatilization differences contribute to the observed variability in the distribution of reduced and oxidized N_r. Additionally, we compare our in situ field estimates of N_r EFs to previous lab and field studies. For similar fuel types, we find ΣNH_x EFs are of the same magnitude or larger than lab‐based NH₃ EF estimates, and ΣNO_y EFs are smaller than lab NO_x EFs.

The spectral, spatial and temporal characteristics of the Landsat data record make it appropriate for mapping fire scars. Twenty-two annual fire scar maps from 1972–2002 were produced from historical Landsat imagery for a semi-arid savannah landscape on the South Africa–Botswana border, centred over Madikwe Game Reserve (MGR) in South Africa. A principal components transformation (PCT) helped differentiate the spectral signal of fire scars in each image. A simple, nonparametric, supervised classification (parallelepiped) of the PCT data differentiated burned and unburned areas. During most years, fire occurrences and the percentage of area burned annually were lowest in Botswana, highest in MGR, and intermediate in South Africa outside MGR. These fire scar maps are aiding MGR managers, who are endeavouring to restore a more active fire regime following decades of fire exclusion.

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models.