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Individual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR.The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open‐source web‐based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting‐edge analyses of properties related to forest ecology and management.We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post‐hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest.treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open‐source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function. [ABSTRACT FROM AUTHOR]

We developed a historical natural community map and a spatially explicit ecological condition model (ECM) to evaluate conditions of the Apalachicola National Forest's longleaf pine habitats. We identified and mapped historical vegetation patterns across the forest and then compared current vegetation structure derived from LiDAR and field surveys to desired conditions for the respective habitat types. In the first example of how these tools may be applied, we show how the natural communities map improved our understanding of wet savanna distribution and how the ECM then revealed opportunities and challenges for managing this unique habitat. In the second example, we show that the ECM scores were closely aligned with red-cockaded woodpecker habitat selection at three nested spatial scales relevant for that species' ecology. Both of these analyses demonstrate how historical data and ecological condition assessments improve our understanding of resource patterns and may inform possible management actions. [ABSTRACT FROM AUTHOR]

Lidar data is increasingly available over large spatial extents and can also be combined with satellite imagery to provide detailed vegetation structural metrics. To fully realize the benefits of lidar data, practical and scalable processing workflows are needed. In this study, we used the lidR R software package, a custom forest metrics function in R, and a distributed cloud computing environment to process 11 TB of airborne lidar data covering ~22,900 km2 into 28 height, cover, and density metrics. We combined these lidar outputs with field plot data to model basal area, trees per acre, and quadratic mean diameter. We compared lidar-only models with models informed by spectral imagery only, and lidar and spectral imagery together. We found that lidar models outperformed spectral imagery models for all three metrics, and combination models performed slightly better than lidar models in two of the three metrics. One lidar variable, the relative density of low midstory canopy, was selected in all lidar and combination models, demonstrating the importance of midstory forest structure in the study area. In general, this open-source lidar-processing workflow provides a practical, scalable option for estimating structure over large, forested landscapes. The methodology and systems used for this study offered us the capability to process large quantities of lidar data into useful forest structure metrics in compressed timeframes. [ABSTRACT FROM AUTHOR]

The structure of a forest canopy often reflects its disturbance history. Such signatures of past disturbances or legacies can influence how the ecosystem functions across broad spatio-temporal scales. The 1938 hurricane and ensuing salvage operations which swept through New England represent the most recent large, infrequent disturbance (LID) in this region. Though devastating (downing ∼70% of the timber at Harvard Forest), the disturbance was not indiscriminate; it left behind a heterogeneous landscape comprised of different levels of canopy damage. We analyzed large-footprint LiDAR, from the Prospect Hill tract at Harvard Forest in central Massachusetts, to assess whether damage to the forest structure from the hurricane and subsequent timber extraction could be discerned after ∼65 years. Differences in LiDAR-derived measures of canopy height and vertical diversity were a function of the degree of damage from the 1938 hurricane and the predominant tree species which is, in part, a function of land use history. Higher levels of damage corresponded to slightly shorter canopies with a less even vertical distribution of return from the ground to the top. In addition, differences in canopy topography as revealed by spatial autocorrelation of canopy top heights were found among the damage classes. Less disturbed stands were characterized by lower levels of local autocorrelation for canopy height and higher levels of vertical diversity of LiDAR returns. These differences in canopy structure reveal that the forest tract has not completely recovered from the 1938 LID and salvage regime, which may have implications on arboreal and understory habitat and other ecosystem functions. [Copyright &y& Elsevier]

The article presents a study which assesses the potential for using lidar observations of tropical forest structure to initialize the ecosystem demography model for improved estimates of carbon stocks and fluxes. It indicates that lidar data provided substantial constraints on model estimates of carbon stocks and net carbon fluxes. The results the study provide a promising illustration of the power of combining lidar data on vegetation height with a height-structured ecosystem model.

Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., <12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally. [ABSTRACT FROM AUTHOR]

Temporal series of lidar, properly field-validated, can provide critical information allowing in-ferences about the dynamics of biomass and carbon in forest canopies. Forest canopies gain carbon through net primary production (NPP) and lose carbon through canopy component damage and death, such as fine and coarse woody debris and litterfall (collectively, debris-fall). We describe a statistical method to extract gamma distributions of NPP and debris-fall rates in forest canopies from lidar missions repeated through time and we show that the means of these distributions covary with ecologically meaningful variables: topography, canopy structure, and taxonomic composition. The method employed is the generalized method of moments that applies the R package gmm to uncover the distribution of latent variables. We present an example with eco-logical interpretations that support the method's application to change in biomass estimated for a boreal forest in southcentral Alaska. The deconvolution of net change from remote sensing products as distributions of NPP and debris-fall rates can inform carbon cycling models of can-opy-level NPP and debris-fall rates. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) allow efficient acquisition of forest data at very high resolution at relatively low cost, making it useful for multi-temporal assessment of detailed tree crowns and forest structure. Single-pass flight plans provide rapid surveys for key selected high-priority areas, but their accuracy is still unexplored. We compared aircraft-borne LiDAR with GatorEye UAV-borne LiDAR in the Apalachicola National Forest, USA. The single-pass approach produced digital terrain models (DTMs), with less than 1 m differences compared to the aircraft-derived DTM within a 145° field of view (FOV). Canopy height models (CHM) provided reliable information from the top layer of the forest, allowing reliable treetop detection up to wide angles; however, underestimations of tree heights were detected at 175 m from the flightline, with an error of 2.57 ± 1.57. Crown segmentation was reliable only within a 60° FOV, from which the shadowing effect made it unviable. Reasonable quality threshold values for LiDAR products were: 195 m (145° FOV) for DTMs, 95 m (110° FOV) for CHM, 160 to 180 m (~140° FOV) for ITD and tree heights, and 40 to 60 m (~60° FOV) for crown delineation. These findings also support the definition of mission parameters for standard grid-based flight plans under similar forest types and flight parameters. [ABSTRACT FROM AUTHOR]

Lidar remote sensing, which directly measures vertical forest structure, is a breakthrough technology with many forestry applications. Using the laser light equivalent of radar, lidar instruments accuratelyestimate such important forest structural characteristics as canopy heights, stand volume, basal area, and above-ground biomass. And because subcanopy vegetation height is a function of species composition,climate, and site quality, the results can be used for land cover classification, habitat mapping, and forest wildlife management. [ABSTRACT FROM AUTHOR]

An objective, quantifiable index of structural biodiversity that could be rapidly obtained with reduced or no field effort is essential for the use of structure as universal ecological indicator for ecosystem management. Active remote sensing provides a rapid assessment tool to potentially guide land managers in highly dynamic and spatially complex landscapes. These landscapes are often dependent on frequent disturbance regimes and characterized by high endemism. We propose a modified Shannon–Wiener Index and modified Evenness Index as stand structural complexity indices for surrogates of ecosystem health. These structural indices are validated at Tall Timbers Research Station the site of one of the longest running fire ecology studies in southeastern U.S. This site is dominated by highly dynamic pine-grassland woodlands maintained with frequent fire. Once the dominant ecosystem in the Southeast, this woodland complex has been cleared for agriculture or converted to other cover types, and depends on a frequent (1- to 3-year fire return interval) low- to moderate-intensity fire regime to prevent succession to mixed hardwood forests and maintain understory species diversity. Structural evaluation of the impact of multiple disturbance regimes included height profiles and derived metrics for five different fire interval treatments; 1-year, 2-year, 3-year, mixed fire frequency (a combination of 2- and 4-year fire returns), and fire exclusion. The 3-dimensional spatial arrangement of structural elements was used to assess hardwood encroachment and changes in structural complexity. In agreement with other research, 3-year fire return interval was considered to be the best fire interval treatment for maintaining the pine-grassland woodlands, because canopy cover and vertical diversity indices were shown to be statistically higher in fire excluded and less frequently burned plots than in 1- and 2-year fire interval treatments. We developed a LiDAR-derived structural diversity index, LHDI, and propose that an ecosystem-specific threshold target for management intervention can be developed, based on significant shifts in structure and composition using this new index. Structural diversity indices can be valuable surrogates of ecosystem biodiversity, and ecosystem-specific target values can be developed as objective quantifiable goals for conservation and ecosystem integrity, particularly in remote areas. [ABSTRACT FROM AUTHOR]

Abstract: Advances in remote sensing and space-based imaging have led to an increased understanding of past settlements and landscape use, but – until now – the images in tropical regions have not been detailed enough to provide datasets that permitted the computation of digital elevation models for heavily forested and hilly terrain. The application of airborne LiDAR (light detection and ranging) remote sensing provides a detailed raster image that mimics a 3-D view (technically, it is 2.5-D) of a 200 sq km area covering the settlement of Caracol, a long-term occupied (600 BC-A.D. 250–900) Maya archaeological site in Belize, literally “seeing” though gaps in the rainforest canopy. Penetrating the encompassing jungle, LiDAR-derived images accurately portray not only the topography of the landscape, but also, structures, causeways, and agricultural terraces – even those with relatively low relief of 5–30 cm. These data demonstrate the ability of the ancient Maya to modify, radically, their landscape in order to create a sustainable urban environment. Given the time and intensive effort involved in producing traditional large-scale maps, swath mapping LiDAR is a powerful cost-efficient tool to analyze past settlement and landscape modifications in tropical regions as it covers large study areas in a relatively short time. The use of LiDAR technology, as illustrated here, will ultimately replace traditional settlement mapping in tropical rainforest environments, such as the Maya region, although ground verification will continue to be necessary to test its efficacy. [ABSTRACT FROM AUTHOR]