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Generating accurate and unbiased wall-to-wall canopy height maps from airborne lidar data for large regions is useful to forest scientists and natural resource managers. However, mapping large areas often involves using lidar data from different projects, with varying acquisition parameters. In this work, we address the important question of whether one can accurately model canopy heights over large areas of the Southeastern US using a very heterogeneous dataset of small-footprint, discrete-return airborne lidar data (with 76 separate lidar projects). A unique aspect of this effort is the use of nationally uniform and extensive field data (~1800 forested plots) from the Forest Inventory and Analysis (FIA) program of the US Forest Service. Preliminary results are quite promising: Over all lidar projects, we observe a good correlation between the 85th percentile of lidar heights and field-measured height (r = 0.85). We construct a linear regression model to predict subplot-level dominant tree heights from distributional lidar metrics (R2 = 0.74, RMSE = 3.0 m, n = 1755). We also identify and quantify the importance of several factors (like heterogeneity of vegetation, point density, the predominance of hardwoods or softwoods, the average height of the forest stand, slope of the plot, and average scan angle of lidar acquisition) that influence the efficacy of predicting canopy heights from lidar data. For example, a subset of plots (coefficient of variation of vegetation heights <0.2) significantly reduces the RMSE of our model from 3.0–2.4 m (~20% reduction). We conclude that when all these elements are factored into consideration, combining data from disparate lidar projects does not preclude robust estimation of canopy heights.

Forest inventories play an important role in enabling informed decisions to be made for the management and conservation of forest resources; however, the process of collecting inventory information is laborious. Despite advancements in mapping technologies allowing forests to be digitized in finer granularity than ever before, it is still common for forest measurements to be collected using simple tools such as calipers, measuring tapes, and hypsometers. Dense understory vegetation and complex forest structures can present substantial challenges to point cloud processing tools, often leading to erroneous measurements, and making them of less utility in complex forests. To address this challenge, this research demonstrates an effective deep learning approach for semantically segmenting high-resolution forest point clouds from multiple different sensing systems in diverse forest conditions. Seven diverse point cloud datasets were manually segmented to train and evaluate this model, resulting in per-class segmentation accuracies of Terrain: 95.92%, Vegetation: 96.02%, Coarse Woody Debris: 54.98%, and Stem: 96.09%. By exploiting the segmented point cloud, we also present a method of extracting a Digital Terrain Model (DTM) from such segmented point clouds. This approach was applied to a set of six point clouds that were made publicly available as part of a benchmarking study to evaluate the DTM performance. The mean DTM error was 0.04 m relative to the reference with 99.9% completeness. These approaches serve as useful steps toward a fully automated and reliable measurement extraction tool, agnostic to the sensing technology used or the complexity of the forest, provided that the point cloud has sufficient coverage and accuracy. Ongoing work will see these models incorporated into a fully automated forest measurement tool for the extraction of structural metrics for applications in forestry, conservation, and research.

Recent evidence suggests that prosocial behaviors like conditional cooperation and costly norm enforcement can stabilize large-scale cooperation for commons management. However, field evidence on the extent to which variation in these behaviors among actual commons users accounts for natural commons outcomes is altogether missing. Here, we combine experimental measures of conditional cooperation and survey measures on costly monitoring among 49 forest user groups in Ethiopia with measures of natural forest commons outcomes to show that (i) groups vary in conditional cooperator share, (ii) groups with larger conditional cooperator share are more successful in forest commons management, and (iii) costly monitoring is a key instrument with which conditional cooperators enforce cooperation. Our findings are consistent with models of gene-culture coevolution on human cooperation and provide external validity to laboratory experiments on social dilemmas.

Forest resource information has a hierarchical structure: individual tree attributes are summed at the plot level and then in turn, plot-level estimates are used to derive stand or large-area estimates of forest resources. Due to this hierarchy, it is imperative that individual tree attributes are measured with accuracy and precision. With the widespread use of different measurement tools, it is also important to understand the expected degree of precision associated with these measurements. The most prevalent tree attributes measured in the field are tree species, stem diameter-at-breast-height (dbh), and tree height. For dbh and height, the most commonly used measuring devices are calipers and clinometers, respectively. The aim of our study was to characterize the precision of individual tree dbh and height measurements in boreal forest conditions when using calipers and clinometers. The data consisted of 319 sample trees at a study area in Evo, southern Finland. The sample trees were measured independently by four trained mensurationists. The standard deviation in tree dbh and height measurements was 0.3 cm (1.5%) and 0.5 m (2.9%), respectively. Precision was also assessed by tree species and tree size classes; however, there were no statistically significant differences between the mensurationists for dbh or height measurements. Our study offers insights into the expected precision of tree dbh and height as measured with the most commonly used devices. These results are important when using sample plot data in forest inventory applications, especially now, at a time when new tree attribute measurement techniques based on remote sensing are being developed and compared to the conventional caliper and clinometer measurements.

Questions: Did the forest area in the Swiss Alps increase between 1985 and 1997? Does the forest expansion near the tree line represent an invasion into abandoned grasslands (ingrowth) or a true upward shift of the local tree line? What land cover / land use classes did primarily regenerate to forest, and what forest structural types did primarily regenerate? And, what are possible drivers of forest regeneration in the tree line ecotone, climate and/or land use change? Location: Swiss Alps. Methods: Forest expansion was quantified using data from the repeated Swiss land use statistics GEOSTAT. A moving window algorithm was developed to distinguish between forest ingrowth and upward shift. To test a possible climate change influence, the resulting upward shifts were compared to a potential regional tree line. Results: A significant increase of forest cover was found between 1650 m and 2450 m. Above 1650 m, 10% of the new forest areas were identified as true upward shifts whereas 90% represented ingrowth, and we identified both land use and climate change as likely drivers. Most upward shift activities were found to occur within a band of 300 m below the potential regional tree line, indicating land use as the most likely driver. Only 4% of the upward shifts were identified to rise above the potential regional tree line, thus indicating climate change. Conclusions: Land abandonment was the most dominant driver for the establishment of new forest areas, even at the tree line ecotone. However, a small fraction of upwards shift can be attributed to the recent climate warming, a fraction that is likely to increase further if climate continues to warm, and with a longer time-span between warming and measurement of forest cover. Nomenclature: Aeschimann & Heitz (1996).

1. Terrestrial laser scanning (TLS) captures the three-dimensional structure of habitats. Compared to traditional methods of forest mensuration, it allows quantification of structure at increased resolution, and the derivation of novel metrics with which to inform ecological studies and habitat management. 2. Lowland woodlands in the UK have altered in structure over the last century due to increased abundance of deer and a decline in management. We compared whole-canopy profiles between woodlands with high (>10 deer km⁻²) and low deer density (c. 1 deer km⁻²), and in stands with and without records of management interventions in the last 20 years, providing a test case for the application of TLS in habitat assessment for conservation and management. 3. Forty closed-canopy lowland woodlands (height range 16·5-29·4 m) were surveyed using TLS in two regions of the UK, divided into areas of high- and low-deer abundance, and between plots which had been recently managed or were unmanaged. Three-dimensional reconstructions of the woodlands were created to document the density of foliage and stem material across the entire vertical span of the canopy. 4. There was a 68% lower density of understorey foliage (0·5-2 m above-ground) in high-deer woodlands, consistent in both regions. Despite this, total amounts of foliage detected across the full canopy did not differ between deer density levels. High-deer sites were 5 m taller overall and differed in the distribution of foliage across their vertical profile. Managed woodlands, in contrast, exhibited relatively minor differences from controls, including a lower quantity of stem material at heights from 2 to 5 m, but no difference in foliage density. All main effects were replicated equally in both regions despite notable differences in stand structures between them. 5. Synthesis and applications. Terrestrial laser scanning allows ecologists to move beyond two-dimensional measures of vegetation structure and quantify patterns across complex, heterogeneous, three-dimensional habitats. Our findings suggest that reduction of deer populations is likely to have a strong impact on woodland structures and aid in restoring the complex understorey habitats required by many birds, whereas management interventions as currently practiced have limited and inconsistent effects.

Plot-scale measurements have been the foundation for forest surveys and reporting for over 200 years. Through recent integration with airborne and satellite remote sensing, manual measurements of vegetation structure at the plot scale are now the basis for landscape, continental and international mapping of our forest resources. The use of terrestrial laser scanning (TLS) for plot-scale measurement was first demonstrated over a decade ago, with the intimation that these instruments could replace manual measurement methods. This has not yet been the case, despite the unparalleled structural information that TLS can capture. For TLS to reach its full potential, these instruments cannot be viewed as a logical progression of existing plot-based measurement. TLS must be viewed as a disruptive technology that requires a rethink of vegetation surveys and their application across a wide range of disciplines. We review the development of TLS as a plot-scale measurement tool, including the evolution of both instrument hardware and key data processing methodologies. We highlight two broad data modelling approaches of gap probability and geometrical modelling and the basic theory that underpins these. Finally, we discuss the future prospects for increasing the utilisation of TLS for plot-scale forest assessment and forest monitoring.

Accurate estimates of aboveground biomass (AGB) are essential for forest policies to reduce carbon emissions. Unmanned aerial laser scanning (UAV-LS) offers unprecedented millimetric detail but is underutilized in monitoring broadleaf Mediterranean forests compared to coniferous ones. This study aims to design and evaluate a procedure for AGB estimates based on the predictive power of crown features. In the first step, we manually created Quantitative Structure Models (QSMs) for 320 trees using data from UAV laser scanning (UAV-LS), airborne laser scanning (ALS), and co-registered terrestrial laser scanning (TLS). This provided the most accurate non-destructive estimate of aboveground biomass (AGB) in the absence of destructive measurements. For each reference tree we also measured crown projection and crown volume to build two separated models relating AGB to such crown features. In the second phase, we evaluated the potential of UAV-LS for quantifying AGB in a pure European beech (Fagus sylvatica) forest and compared it with traditional ALS estimates, using fully automatic procedures. The two obtained tree-level AGB models were then tested using three datasets derived from 35 sampling plots over the same study area: (a) 1130 trees manually segmented (phase-2 reference); (b) trees automatically extracted from ALS data; and (c) trees automatically extracted from UAV-LS data. Results demonstrate that detailed UAV-LS data improve model sensitivity compared to ALS data (RMSE = 45.6 Mg ha−1, RMSE% = 13.4%, R2 = 0.65, for the best ALS model; RMSE = 44.0 Mg ha−1, RMSE% = 12.9%, R2 = 0.67, for the best UAV-LS model), allowing for the detection of AGB differences even in quite homogenous forest structures. Overall, this study demonstrates the combined use of both laser scanner data can foster non-destructive and more precise AGB estimation than the use of only one, in forested areas across hectare scales (1 to 100 ha).

Quantifying nitrogen uptake rates across different forest types is critical for a range of ecological questions, including the parameterization of global climate change models. However, few measurements of forest nitrogen uptake rates are available due to the intensive labor required to collect in situ data. Here, we seek to optimize data collection efforts by identifying measurements that must be made in situ and those that can be omitted or approximated from databases. We estimated nitrogen uptake rates in 18 mature monodominant forest stands comprising 13 species of diverse taxonomy at the Morton Arboretum in Lisle, IL, USA. We measured all nitrogen concentrations, foliage allocation, and fine root biomass in situ. We estimated wood biomass increments by in situ stem diameter and stem core measurements combined with allometric equations. We estimated fine root turnover rates from database values. We analyzed similar published data from monodominant forest FACE sites. At least in monodominant forests, accurate estimates of forest nitrogen uptake rates appear to require in situ measurements of fine root productivity and are appreciably better paired with in situ measurements of foliage productivity. Generally, wood productivity and tissue nitrogen concentrations may be taken from trait databases at higher taxonomic levels. Careful sorting of foliage or fine roots to species is time consuming but has little effect on estimates of nitrogen uptake rate. By directing research efforts to critical in situ measurements only, future studies can maximize research effort to identify the drivers of varied nitrogen uptake patterns across gradients.

Tropical forest above‐ground wood production (AGWP) varies substantially along environmental gradients. Some evidence suggests that AGWP may vary between regions and specifically that Asian forests have particularly high AGWP. However, comparisons across biogeographic regions using standardized methods are lacking, limiting our assessment of pan‐tropical variation in AGWP and potential causes. We sampled AGWP in NW Amazon (17 long‐term forest plots) and N Borneo (11 plots), both with abundant year‐round precipitation. Within each region, forests growing on a broad range of edaphic conditions were sampled using standardized soil and forest measurement techniques. Plot‐level AGWP was 49% greater in Borneo than in Amazonia (9.73 ± 0.56 vs. 6.53 ± 0.34 Mg dry mass ha⁻¹ a⁻¹, respectively; regional mean ± 1 SE). AGWP was positively associated with soil fertility (PCA axes, sum of bases and total P). After controlling for the edaphic environment, AGWP remained significantly higher in Bornean plots. Differences in AGWP were largely attributable to differing height–diameter allometry in the two regions and the abundance of large trees in Borneo. This may be explained, in part, by the greater solar radiation in Borneo compared with NW Amazonia. Trees belonging to the dominant SE Asian family, Dipterocarpaceae, gained woody biomass faster than otherwise equivalent, neighbouring non‐dipterocarps, implying that the exceptional production of Bornean forests may be driven by floristic elements. This dominant SE Asian family may partition biomass differently or be more efficient at harvesting resources and in converting them to woody biomass. Synthesis. N Bornean forests have much greater AGWP rates than those in NW Amazon when soil conditions and rainfall are controlled for. Greater resource availability and the highly productive dipterocarps may, in combination, explain why Asian forests produce wood half as fast again as comparable forests in the Amazon. Our results also suggest that taxonomic groups differ in their fundamental ability to capture carbon and that different tropical regions may therefore have different carbon uptake capacities due to biogeographic history.