Investigating the influence of LiDAR ground surface errors on the utility of derived forest inventories

Light detection and ranging, or LiDAR, effectively produces products spatially characterizing both terrain and vegetation structure; however, development and use of those products has outpaced our understanding of the errors within them. LiDAR’s ability to capture three-dimensional structure has led to interest in conducting or augmenting forest inventories with LiDAR data. Prior to applying LiDAR in operational management, it is necessary to understand the errors in Li- DAR-derived estimates of forest inventory metrics (i.e., tree height). Most LiDAR-based forest inventory metrics require creation of digital elevation models (DEM), and because metrics are calculated relative to the DEM surface, errors within the DEMs propagate into delivered metrics. This study combines LiDAR DEMs and 54 ground survey plots to investigate how surface morphology and vegetation structure influence DEM errors. The study further compared two LiDAR classification algorithms and found no significant difference in their performance. Vegetation structure was found to have no influence, whereas increased variability in the vertical error was observed on slopes exceeding 30°, illustrating that these algorithms are not limited by high-biomass western coniferous forests, but that slope and sensor accuracy both play important roles. The observed vertical DEM error translated into ±1%–3% error range in derived timber volumes, highlighting the potential of LiDAR-derived inventories in forest management

Managing for climate change on federal lands of the western United States: perceived usefulness of climate science, effectiveness of adaptation strategies, and barriers to implementation

Recent mandates in the United States require federal agencies to incorporate climate change science into land management planning efforts. These mandates target possible adaptation and mitigation strategies. However, the degree to which climate change is actively being considered in agency planning and management decisions is largely unknown. We explored the usefulness of climate change science for federal resource managers, focusing on the efficacy of potential adaptation strategies and barriers limiting the use of climate change science in adaptation efforts. Our study was conducted in the northern Rocky Mountains region of the western United States, where we interacted with 77 U.S. Forest Service and Bureau of Land Management personnel through surveys, semistructured interviews, and four collaborative workshops at locations across Idaho and Montana. We used a mixed-methods approach to evaluate managers' perceptions about adapting to and mitigating for climate change. Although resource managers incorporate general language about climate change in regional and landscape-level planning documents, they are currently not planning on-the-ground adaptation or mitigation projects. However, managers felt that their organizations were most likely to adapt to climate change through use of existing management strategies that are already widely implemented for other non climate-related management goals. These existing strategies, (e.g., thinning and prescribed burning) are perceived as more feasible than new climate-specific methods (e.g., assisted migration) because they already have public and agency support, accomplish multiple goals, and require less anticipation of the future timing and probability of climate change impacts. Participants reported that the most common barriers to using climate change information included a lack of management-relevant climate change science, inconsistent agency guidance, and insufficient time and resources to access, interpret, and apply current climate science information to management plans.Key words: adaptation; Bureau of Land Management; climate change; decision making; Forest Service; land management; public landsThis is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Resilience Alliance. The published article can be found at: http://www.ecologyandsociety.org

Correlation between the Josephson coupling energy and the condensation energy in bilayer cuprate superconductors

We review some previous studies concerning the intra-bilayer Josephson plasmons and present new ellipsometric data of the c-axis infrared response of almost optimally doped Bi_{2}Sr_{2}CaCu_{2}O_{8}. The c-axis conductivity of this compound exhibits the same kind of anomalies as that of underdoped YBa_{2}Cu_{3}O_{7-delta}. We analyze these anomalies in detail and show that they can be explained within a model involving the intra-bilayer Josephson effect and variations of the electric field inside the unit cell. The Josephson coupling energies of different bilayer compounds obtained from the optical data are compared with the condensation energies and it is shown that there is a reasonable agreement between the values of the two quantities. We argue that the Josephson coupling energy, as determined by the frequency of the intra-bilayer Josephson plasmon, represents a reasonable estimate of the change of the effective c-axis kinetic energy upon entering the superconducting state. It is further explained that this is not the case for the estimate based on the use of the simplest ``tight-binding'' sum rule. We discuss possible interpretations of the remarkable agreement between the Josephson coupling energies and the condensation energies. The most plausible interpretation is that the interlayer tunneling of the Cooper pairs provides the dominant contribution to the condensation energy of the bilayer compounds; in other words that the condensation energy of these compounds can be accounted for by the interlayer tunneling theory. We suggest an extension of this theory, which may also explain the high values of T_{c} in the single layer compounds Tl_{2}Ba_{2}CuO_{6} and HgBa_{2}CuO_{4}, and we make several experimentally verifiable predictions.Comment: 16 pages (including Tables) and 7 figures; accepted for publication in Physical Review

Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents

Long-Term Impacts of Fuel Treatment Placement with Respect to Forest Cover Type on Potential Fire Behavior across a Mountainous Landscape

Research Highlights: The impact of variation in fuels and fuel dynamics among forest cover types on the outcome of fuel treatments is poorly understood. This study investigated the potential effects of treatment placement with respect to cover type on the development of potential fire behavior over time for 48 km2 of forest in Colorado, USA. Our findings can inform the placement of fuel treatments in similar forests to maximize their effectiveness and longevity. Background and Objectives: Efficient placement of fuel treatments is essential to maximize the impact of limited resources for fuels management. We investigated how the placement of treatments with respect to forest cover type affected the rate of spread, size, and prevalence of different fire types for simulated wildfires for 50 years after treatment. Materials and Methods: We generated an analysis landscape consisting of two cover types: stands on southerly aspects had low rates of tree growth and regeneration compared to stands on northerly aspects. We then simulated 1) thinning treatments across 20% of the landscape, with treatments exclusively located on either southerly (‘south treatment’) or northerly (‘north treatment’) aspects; 2) subsequent tree growth and regeneration; and 3) wildfires at 10-year intervals. Finally, we used metrics of fuel hazard and potential fire behavior to understand the interplay between stand-level fuel dynamics and related impacts to potential fire behavior across the broader landscape. Results: Although post-treatment metrics of stand-level fuel hazard were similar among treatment scenarios, only the south treatment reduced rates of fire spread and fire size relative to no treatment. Most differences in modeled fire behavior between treatment scenarios disappeared after two decades, despite persistently greater rates of stand-level fuel hazard development post-treatment for the north treatment. For all scenarios, the overall trajectory was of shrinking fires and less crown fire behavior over time, owing to crown recession in untreated stands. Conclusions: Systematic differences among cover types, such as those in our study area, have the potential to influence fuel treatment outcomes. However, complex interactions between treatment effects, topography, and vegetation structure and dynamics warrant additional study

Spatial variability of surface fuels in treated and untreated ponderosa pine forests of the southern Rocky Mountains

There is growing consensus that spatial variability in fuel loading at scales down to 0.5 m may govern fire behaviour and effects. However, there remains a lack of understanding of how fuels vary through space in wildland settings. This study quantifies surface fuel loading and its spatial variability in ponderosa pine sites before and after fuels treatment in the southern Rocky Mountains, USA. We found that spatial semivariance for 1- and 100-h fuels, litter and duff following thin-and-burn treatments differed from untreated sites, and was lower than thin-only sites for all fuel components except 1000-h fuels. Fuel component semivariance increased with mean fuel component loading. The scale of spatial autocorrelation for all fuel components and sites ranged from \u3c1 to 48 m, with the shortest distances occurring for the finest fuel components (i.e. duff, litter). Component mean fuel particle diameter strongly predicted (R2 = 0.88) the distance needed to achieve sample independence. Additional work should test if these scaling relationships hold true across forested ecosystems, and could reveal fundamental processes controlling surface fuel variability. Incorporating knowledge of spatial variability into fuel sampling protocols will enhance assessment of wildlife habitat, and fire behaviour and effects modelling, over singular stand-level means

A Comparison of Two Open Source LiDAR Surface Classification Algorithms

With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are openly available, free to use, and are supported by published results. Two of the latter are the multiscale curvature classification and the Boise Center Aerospace Laboratory LiDAR (BCAL) algorithms. This study investigated the accuracy of these two algorithms (and a combination of the two) to create a digital terrain model from a raw LiDAR point cloud in a semi-arid landscape. Accuracy of each algorithm was assessed via comparison with \u3e7,000 high precision survey points stratified across six different cover types. The overall performance of both algorithms differed by only 2%; however, within specific cover types significant differences were observed in accuracy. The results highlight the accuracy of both algorithms across a variety of vegetation types, and ultimately suggest specific scenarios where one approach may outperform the other. Each algorithm produced similar results except in the ceanothus and conifer cover types where BCAL produced lower errors

A Comparison of Four Spatial Interpolation Methods for Modeling Fine-Scale Surface Fuel Load in a Mixed Conifer Forest with Complex Terrain

Patterns of spatial heterogeneity in forests and other fire-prone ecosystems are increasingly recognized as critical for predicting fire behavior and subsequent fire effects. Given the difficulty in sampling continuous spatial patterns across scales, statistical approaches are common to scale from plot to landscapes. This study compared the performance of four spatial interpolation methods (SIM) for mapping fine-scale fuel loads: classification (CL), multiple linear regression (LR), ordinary kriging (OK), and regression kriging (RK). These methods represent commonly used SIMs and demonstrate a diversity of non-geostatistical, geostatistical, and hybrid approaches. Models were developed for a 17.6-hectare site using a combination of metrics derived from spatially mapped trees, surface fuels sampled with an intensive network of photoload plots, and topographic variables. The results of this comparison indicate that all estimates produced unbiased spatial predictions. Regression kriging outperformed the other approaches that either relied solely on interpolation from point observations or regression-based approaches using auxiliary information for developing fine-scale surface fuel maps. While our analysis found that surface fuel loading was correlated with species composition, forest structure, and topography, the relationships were relatively weak, indicating that other variables and spatial interactions could significantly improve surface fuel mapping

High resolution mapping of development in the wildland-urban interface using object based image extraction

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m2) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting