Tree mortality from major disturbances can greatly increase dead wood in forested areas, affecting fire intensity and behavior, wildlife habitat, and carbon dynamics. Accurately quantifying regional standing dead tree (SDT) pools, as conducted by the U.S. Forest Service Forest Inventory and Analysis (FIA) program, remains a prominent challenge. Little empirical work has been done accounting for structural changes in SDT volume across decay classes due to measurement and sampling challenges associated with SDT. Light-detection-and-ranging (LiDAR) represents a remote sensing technology with the potential to improve sampling efficacy and provide volume estimates of SDT via non-destructive sampling. Following this, the goal of this study was to explore the feasibility of empirically quantifying and assessing structural volume in southern pine SDT by decay class using terrestrial LiDAR. To meet this goal, we addressed three objectives, 1) construct empirical volume estimates of SDT by decay class using terrestrial LiDAR and a voxel-based, volume calculation algorithm capable of accounting for occlusion and point cloud quality, 2) develop allometric relationships of aboveground SDT component volumes by decay class and assess error in models and predictions, and 3) quantify proportion-remaining volume of SDT components from terrestrial LiDAR-derived volumes relative to predicted intact tree volumes. This study represents the first to develop empirically-based, terrestrial LiDAR-derived allometric volume relationships and proportion-remaining volume of SDT by decay class. Results indicate that terrestrial LiDAR-derived volumes of SDT produced robust allometric equations by decay class for total above-stump and stem-plus-bark components (adjusted R-2 = 0.94-0.98). Allometric relationships for tops-and-branches comprised more variability, likely impacted by scan quality, having adjusted R-2 values of similar to 0.52-0.59. Notably, the inclusion of height in allometric relationships for total above-stump volume precluded the need for decay class as a covariate, accounting for the variability inherent in each decay class. Importantly, this means that total above-stump allometric equations could be effective under different decay class systems or, more broadly, where no decay classes were measured, thus providing broad utility. Empirically-derived proportion-remaining volume of SDT components followed expected decreasing trends by decay class. Interestingly, proportion-remaining volume for tops-and-branches closely matched theoretically-derived values from a previous FIA-related study. Ultimately, terrestrial LiDAR was critical for efficiently measuring volume of southern pine SDT by decay class and for developing SDT-specific allometric relationships of volume and estimates of structural change by decay class. This study showcases the feasibility of LiDAR-derived, SDT-specific tools for improved accounting of SDT resources in FIA and other inventories.

The structural loss rates of standing dead trees (SDTs) affect a variety of processes of interest to ecologists and foresters, yet the decomposition of SDTs has been traditionally characterized by qualitative decay classes, reductions in wood density as decay progresses, and sampling schemes focused on estimating snag longevity. By establishing a methodology to accurately and efficiently quantify SDT structural loss over time, these estimated structural loss rates would improve the performance of a variety of models and potentially provide new insight as to the manner in which SDTs undergo degradation in various conditions. The specific objective of this study were: 1) utilize the TreeVoIX algorithm to estimate the volume of 29 SDTs scanned with terrestrial lidar; 2) develop a novel, voxel-based change detection algorithm capable of providing automated structural loss estimates with multitemporal terrestrial lidar observations; and 3) estimate and characterize the structural loss rates of Pinus taeda and Quercus stellata in southeastern Texas. A voxel-based change detection methodology was developed to accurately detect and quantify structural losses and incorporated several methods to mitigate the challenges presented by shifting tree and branch positions as SDT decay progresses. The volume and structural loss of 29 SDTs, composed of Pinus taeda and Quercus stellata, were successfully estimated using multitemporal terrestrial lidar observations over elapsed times ranging from 71 to 753 days. Pine and oak structural loss rates were characterized by estimating the amount of volumetric loss occurring in 20 equal-interval height bins of each SDT. Results showed that large pine snags exhibited more rapid structural loss in comparison to medium-sized oak snags in southeastern Texas.

Standing dead trees (SDTs) are an important forest component and impact a variety of ecosystem processes, yet the carbon pool dynamics of SDTs are poorly constrained in terrestrial carbon cycling models. The ability to model wood decay and carbon cycling in relation to detectable changes in tree structure and volume over time would greatly improve such models. The specific objectives of this paper were to: 1) develop an automated SDT volume estimation algorithm providing accurate volume estimates for trees scanned with terrestrial lidar in dense forests; 2) assess the volume estimation algorithm's accuracy with respect to large and small branches; and 3) characterize the impact of occlusion with regards to volume estimation accuracy and the ability of the algorithm to mitigate challenges posed by lower quality point clouds. A voxel-based volume estimation algorithm, "TreeVoIX," was developed and incorporates several methods designed to robustly process point clouds of varying quality levels. The algorithm operates on horizontal voxel slices by segmenting the slice into distinct branch or stem sections then applying an adaptive contour interpolation and interior filling process to create solid reconstructed tree models. The concept of vertical point cloud resampling is introduced to facilitate the modeling of lower quality point clouds with a small voxel size. TreeVolX estimated large and small branch volume with a root-mean-square error of 7.3% and 13.8%, respectively, and the adaptive contour interpolation was shown to significantly reduce volume estimation errors in the case of significantly occluded point clouds.

Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: (1) direct decomposition, (2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms – the Richardson-Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from the corresponding reference data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22m for DTMs, <0.77m for CHMs) and root mean square error (RMSE) (<1.26m for DTMs, <1.93m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (<1.01m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE. Additionally, the high level of uncertainty occurs more on areas with high slope and high vegetation. This study provides an alternative and innovative approach for waveform processing that will benefit high fidelity processing of waveform LiDAR data to characterize vegetation structures.

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.