•We present lidar system types and some considerations with regards to applications.•Current usage of lidar in forest ecology and productivity research is briefly reviewed.•Key issues for effective ...use of lidar in forest ecosystem science are identified.•Pathways for further promoting the role of lidar in advancing knowledge are suggested.
Forest structure is an important driver of ecosystem dynamics, including the exchange of carbon, water and energy between canopies and the atmosphere. Structural descriptors are also used in numerous studies of ecological processes and ecosystem services. Over the last 20+ years, lidar technology has fundamentally changed the way we observe and describe forest structure, and it will continue to impact the ways in which we investigate and monitor the relations between forest structure and functions. Here we present the currently available lidar system types (ground, air, and space-based), we highlight opportunities and challenges associated with each system, as well as challenges associated with a wider use of lidar technology and wider availability of lidar derived products. We also suggest pathways for lidar to further contribute to addressing questions in forest ecosystem science and increase benefits to a wider community of researchers.
Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure ...information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of forest structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical forest structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and forest inventory data. The forest structure maps obtained indicated the potential to distinguish between different forest stages and the identification of different types of forest structure changes induced by logging, natural disturbance, or forest management.
Monitoring and managing Earth’s forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial ...campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-m resolution, using freely available satellite imagery as input. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic-aperture radar images into maps of five different forest structure variables: 95th height percentile, mean height, density, Gini coefficient, and fractional cover. We train and test our model on reference data from 41 airborne laser scanning missions across Norway and demonstrate that it is able to generalize to unseen test regions, achieving normalized mean absolute errors between 11% and 15%, depending on the variable. Our work is also the first to propose a variant of so-called Bayesian deep learning to densely predict multiple forest structure variables with well-calibrated uncertainty estimates from satellite imagery. The uncertainty information increases the trustworthiness of the model and its suitability for downstream tasks that require reliable confidence estimates as a basis for decision making. We present an extensive set of experiments to validate the accuracy of the predicted maps as well as the quality of the predicted uncertainties. To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.
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•Leaf area density (LAD) is closely tied to critical forest processes.•LAD estimates inform management decisions related to forest health and resilience.•LiDAR point clouds from two common sensors ...(NASA G-LiHT & NEON AOP) were compared.•At coarser spatial resolutions, there were minimal differences in LAD estimates.•Open-source and reproducible methodology is provided as an R package (canopyLazR).
Forest processes that play an essential role in carbon sequestration, such as light use efficiency, photosynthetic capacity, and trace gas exchange, are closely tied to the three-dimensional structure of forest canopies. However, the vertical distribution of leaf traits is not uniform; leaves at varying vertical positions within the canopy are physiologically unique due to differing light and environmental conditions, which leads to higher carbon storage than if light conditions were constant throughout the canopy. Due to this within-canopy variation, three-dimensional structural traits are critical to improving our estimates of global carbon cycling and storage by Earth system models and to better understanding the effects of disturbances on carbon sequestration in forested ecosystems. In this study, we describe a reproducible and open-source methodology using the R programming language for estimating leaf area density (LAD; the total leaf area per unit of volume) from airborne LiDAR. Using this approach, we compare LAD estimates at the Smithsonian Environmental Research Center in Maryland, USA, from two airborne LiDAR systems, NEON AOP and NASA G-LiHT, which differ in survey and instrument specifications, collections goals, and laser pulse densities. Furthermore, we address the impacts of the spatial scale of analysis as well as differences in canopy penetration and pulse density on LAD and leaf area index (LAI) estimates, while offering potential solutions to enhance the accuracy of these estimates. LAD estimates from airborne LiDAR can be used to describe the three-dimensional structure of forests across entire landscapes. This information can help inform forest management and conservation decisions related to the estimation of aboveground biomass and productivity, the response of forests to large-scale disturbances, the impacts of drought on forest health, the conservation of bird habitat, as well as a host of other important forest processes and responses.
Global importance of large-diameter trees Lutz, James A.; Furniss, Tucker J.; Johnson, Daniel J. ...
Global ecology and biogeography,
July 2018, Volume:
27, Issue:
7/8
Journal Article
Peer reviewed
Open access
Aim: To examine the contribution of large-diameter trees to biomass, stand structure, and species richness across forest biomes. Location: Global. Time period: Early 21st century. Major taxa studied: ...Woody plants. Methods: We examined the contribution of large trees to forest density, richness and biomass using a global network of 48 large (from 2 to 60 ha) forest plots representing 5,601,473 stems across 9,298 species and 210 plant families. This contribution was assessed using three metrics: the largest 1% of trees ≥ 1 cm diameter at breast height (DBH), all trees ≥ 60 cm DBH, and those rank-ordered largest trees that cumulatively comprise 50% of forest biomass. Results: Averaged across these 48 forest plots, the largest 1% of trees ≥ 1 cm DBH comprised 50% of aboveground live biomass, with hectare-scale standard deviation of 26%. Trees ≥ 60 cm DBH comprised 41% of aboveground live tree biomass. The size of the largest trees correlated with total forest biomass (r2 = .62, p < .001). Large-diameter trees in high biomass forests represented far fewer species relative to overall forest richness (r2 = .45, p < .001). Forests with more diverse large-diameter tree communities were comprised of smaller trees (r2 = .33, p < .001). Lower large-diameter richness was associated with large-diameter trees being individuals of more common species (r2 = .17, p = .002). The concentration of biomass in the largest 1% of trees declined with increasing absolute latitude (r2 = .46, p < .001), as did forest density (r2 = .31, p < .001). Forest structural complexity increased with increasing absolute latitude (r2 = .26, p < .001). Main conclusions: Because large-diameter trees constitute roughly half of the mature forest biomass worldwide, their dynamics and sensitivities to environmental change represent potentially large controls on global forest carbon cycling. We recommend managing forests for conservation of existing large-diameter trees or those that can soon reach large diameters as a simple way to conserve and potentially enhance ecosystem services.
•LiDAR canopy height was extrapolated using SAR and Landsat data on different biomes.•Addition of Landsat data improved results, mainly on temperate conifer forests.•Impacts of resolution, sample ...size, and moisture on model accuracy were evaluated.•Models transferability showed poor performance for site-specific models.•Single biome models showed similar performance to site-specific models.
Spatially-explicit information on forest structure is paramount to estimating aboveground carbon stocks for designing sustainable forest management strategies and mitigating greenhouse gas emissions from deforestation and forest degradation. LiDAR measurements provide samples of forest structure that must be integrated with satellite imagery to predict and to map landscape scale variations of forest structure. Here we evaluate the capability of existing satellite synthetic aperture radar (SAR) with multispectral data to estimate forest canopy height over five study sites across two biomes in North America, namely temperate broadleaf and mixed forests and temperate coniferous forests. Pixel size affected the modelling results, with an improvement in model performance as pixel resolution coarsened from 25m to 100m. Likewise, the sample size was an important factor in the uncertainty of height prediction using the Support Vector Machine modelling approach. Larger sample size yielded better results but the improvement stabilised when the sample size reached approximately 10% of the study area. We also evaluated the impact of surface moisture (soil and vegetation moisture) on the modelling approach. Whereas the impact of surface moisture had a moderate effect on the proportion of the variance explained by the model (up to 14%), its impact was more evident in the bias of the models with bias reaching values up to 4m. Averaging the incidence angle corrected radar backscatter coefficient (γ°) reduced the impact of surface moisture on the models and improved their performance at all study sites, with R2 ranging between 0.61 and 0.82, RMSE between 2.02 and 5.64 and bias between 0.02 and −0.06, respectively, at 100m spatial resolution. An evaluation of the relative importance of the variables in the model performance showed that for the study sites located within the temperate broadleaf and mixed forests biome ALOS-PALSAR HV polarised backscatter was the most important variable, with Landsat Tasselled Cap Transformation components barely contributing to the models for two of the study sites whereas it had a significant contribution at the third one. Over the temperate conifer forests, Landsat Tasselled Cap variables contributed more than the ALOS-PALSAR HV band to predict the landscape height variability. In all cases, incorporation of multispectral data improved the retrieval of forest canopy height and reduced the estimation uncertainty for tall forests. Finally, we concluded that models trained at one study site had higher uncertainty when applied to other sites, but a model developed from multiple sites performed equally to site-specific models to predict forest canopy height. This result suggest that a biome level model developed from several study sites can be used as a reliable estimator of biome-level forest structure from existing satellite imagery.
Mangroves are under immense anthropogenic pressures globally which are further exacerbated by their accessibility to humans. To minimize human access hence pressures to the ecosystem, establishment ...of protected areas is often employed. However, the ecological effectiveness of protected areas, which influences their legal durability, is rarely assessed beyond curbing deforestation. Furthermore, little is known about whether protection could still provide a positive ecological impact if the sites are easily accessible, i.e., adjacent to urban areas, near roads, small in area and/or fragmented. To improve our understanding thereon, this study compares anthropogenic disturbance severity, forest structures and ecosystem carbon (C) stocks of protected and unprotected mangroves near Barranquilla, Colombia’s largest coastal city. The outcomes suggest that accessible, yet protected mangrove has a mean disturbance index of 5.3, lower than unprotected mangrove (mean 11). Protected mangrove also has higher mean (± SD) tree basal area (26.5 ± 15.6 m2 ha−1), mean densities of tree, sapling and seedling (899 ± 398, 5155 ± 7860, and 68,837 ± 73,899 individual ha−1, respectively) and biomass C stock (mean 89.5 ± 39 Mg ha−1) than those of accessible unprotected mangrove (mean basal area 19.3 ± 5 m2 ha−1; mean tree, sapling and seedling densities 823 ± 215, 749 ± 94, and 33,727 ± 44,882 individual ha−1, respectively; mean biomass C stock 60.2 ± 14.5 Mg ha−1). Results suggest that the current sediment C stocks, that is higher in unprotected than protected mangroves (396.8 ± 552.6 and 142.4 ± 205.7 Mg ha−1, respectively), are not primarily driven by conservation status, but by long-term processes that likely pre-date the protected status designation. Mangrove protection, however, could help maintain carbon stocks in soils and biomass and the potential for further soil carbon sequestration, and thus are pivotal in determining future trajectories of mangrove climate mitigation potential. This study shows that even imperfect protection offers ecological benefits to highly accessible ecosystems. Hence, focus should be placed on optimizing these benefits and minimizing their vulnerability to downgrading, downsizing and degazettement.
•influence of protection on mangroves is assessed beyond curbing deforestation.•protection effectively limit anthropogenic disturbance on mangroves.•protection promotes mangrove biomass C and regeneration potential.•mangrove sediment C stocks is largely influenced by sediment properties.•mangrove protection is ecologically beneficial even for highly accessible systems.
Ecological forestry experiments typicaly use large treatment units and silvicultural prescriptions that commonly increase within-unit heterogeneity in structural complexity and species composition in ...large treatment units. Increased heterogeneity influences processes affecting tree responses (e.g., competition) that operate at the neighborhood-level, posing challenges to analysis and interpretation. To investigate whether examining within-unit heterogeneity offers a more meaningful evaluation of project success than comparing categorical treatment effects, we used 20-year data from the Goosenest Adaptive Management Area (AMA) ecological forestry experiment in northern California, U.S. Designed to evaluate management alternatives for reducing fuels and accelerating development of late-seral forest characteristics in ponderosa pine (Pinus ponderosa)/white fir (Abies concolor) mixed-conifer forests, the Goosenest AMA study consists of (1) an untreated Control, (2) a Big Tree treatment using thinning from below to favor retention of large trees of any species, (3) a Pine Emphasis treatment combining thinning from below with radial thinning to favor percent ponderosa pine while increasing structural complexity, and (4) a Pine Emphasis with Fire treatment with added post-thinning prescribed burns. Our objectives were to evaluate 1) how treatments affect within-unit variation in neighborhood competition, structural complexity, and tree species composition, and 2) whether categorical treatment effects versus within-unit variation in competition, complexity, and composition influence individual-tree basal area increment (BAI), vigor as indicated by live crown ratio (ΔLCR), and tree mortality. To accomplish this, we developed and compared a series of generalized linear mixed models. Our analysis included the first investigation into whether fuel-reduction treatments alter tree species mixture-effects in ponderosa pine/white fir mixed-conifer forests. We found that all treatments similarly reduced neighborhood competition relative to the Control. The two Pine Emphasis treatments promoted greater variation in neighborhood competition and higher Percent pine relative to the Big Tree treatment, consistent with restoration objectives. Reduced neighborhood competition improved both ponderosa pine and white fir BAI. Reduced neighborhood competition helped to offset or reverse crown vigor decline in ponderosa pine and white fir, respectively. Species-mixture effects were negative for both small and large ponderosa pine BAI. For white-fir, trees grew faster in neighborhoods with a higher percentage of pine but the probability of mortality increased. Categorical treatment differences consistently reduced ponderosa pine and white fir mortality, except for the Pine Emphasis with Fire treatment, which increased white fir mortality. Our findings suggest that restoring historical ponderosa-pine forest reference conditions could accelerate the development of fire-resistant ponderosa pine tree sizes and sustain large pine growth.
•Fuel reduction treatments foster fine-scale variation in structure and composition.•Local competition influences tree growth and vigor in California mixed-conifers.•Tree size and competition modify species mixture effects on growth.•Tree mortality varies with unit-level treatment and local species composition.•Restoration can benefit disturbance resistance and alter species-mixture effects.
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