Non-intersecting leaf insertion algorithm for tree structure models

We present an algorithm and an implementation to insert broadleaves or needleleaves to a quantitative structure model according to an arbitrary distribution, and a data structure to store the required information efficiently. A structure model contains the geometry and branching structure of a tree. The purpose of the work is to offer a tool for making more realistic simulations with tree models with leaves, particularly for tree models developed from terrestrial laser scan (TLS) measurements. We demonstrate leaf insertion using cylinder-based structure models, but the associated software implementation is written in a way that enables the easy use of other types of structure models. Distributions controlling leaf location, size and angles as well as the shape of individual leaves are user-definable, allowing any type of distribution. The leaf generation process consist of two stages, the first of which generates individual leaf geometry following the input distributions, while in the other stage intersections are prevented by doing transformations when required. Initial testing was carried out on English oak trees to demonstrate the approach and to assess the required computational resources. Depending on the size and complexity of the tree, leaf generation takes between 6 and 18 minutes. Various leaf area density distributions were defined, and the resulting leaf covers were compared to manual leaf harvesting measurements. The results are not conclusive, but they show great potential for the method. In the future, if our method is demonstrated to work well for TLS data from multiple tree types, the approach is likely to be very useful for 3D structure and radiative transfer simulation applications, including remote sensing, ecology and forestry, among others

New perspectives on the ecology of tree structure and tree communities through terrestrial laser scanning

Terrestrial laser scanning (TLS) opens up the possibility of describing the three-dimensional structures of trees in natural environments with unprecedented detail and accuracy. It is already being extensively applied to describe how ecosystem biomass and structure vary between sites, but can also facilitate major advances in developing and testing mechanistic theories of tree form and forest structure, thereby enabling us to understand why trees and forests have the biomass and three-dimensional structure they do. Here we focus on the ecological challenges and benefits of understanding tree form, and highlight some advances related to capturing and describing tree shape that are becoming possible with the advent of TLS. We present examples of ongoing work that applies, or could potentially apply, new TLS measurements to better understand the constraints on optimization of tree form. Theories of resource distribution networks, such as metabolic scaling theory, can be tested and further refined. TLS can also provide new approaches to the scaling of woody surface area and crown area, and thereby better quantify the metabolism of trees. Finally, we demonstrate how we can develop a more mechanistic understanding of the effects of avoidance of wind risk on tree form and maximum size. Over the next few years, TLS promises to deliver both major empirical and conceptual advances in the quantitative understanding of trees and tree-dominated ecosystems, leading to advances in understanding the ecology of why trees and ecosystems look and grow the way they do

Quantifying landscape-level methane fluxes in subarctic Finland using a multiscale approach

Journal ArticleQuantifying landscape-scale methane (CH4) fluxes from boreal and arctic regions, and determining how they are controlled, is critical for predicting the magnitude of any CH4 emission feedback to climate change. Furthermore, there remains uncertainty regarding the relative importance of small areas of strong methanogenic activity, vs. larger areas with net CH4 uptake, in controlling landscape-level fluxes. We measured CH4 fluxes from multiple microtopographical subunits (sedge-dominated lawns, interhummocks and hummocks) within an aapa mire in subarctic Finland, as well as in drier ecosystems present in the wider landscape, lichen heath and mountain birch forest. An intercomparison was carried out between fluxes measured using static chambers, up-scaled using a high-resolution landcover map derived from aerial photography and eddy covariance. Strong agreement was observed between the two methodologies, with emission rates greatest in lawns. CH4 fluxes from lawns were strongly related to seasonal fluctuations in temperature, but their floating nature meant that water-table depth was not a key factor in controlling CH4 release. In contrast, chamber measurements identified net CH4 uptake in birch forest soils. An intercomparison between the aerial photography and satellite remote sensing demonstrated that quantifying the distribution of the key CH4 emitting and consuming plant communities was possible from satellite, allowing fluxes to be scaled up to a 100 km2 area. For the full growing season (May to October), ~ 1.1-1.4 g CH4 m-2 was released across the 100 km2 area. This was based on up-scaled lawn emissions of 1.2-1.5 g CH4 m-2, vs. an up-scaled uptake of 0.07-0.15 g CH4 m-2 by the wider landscape. Given the strong temperature sensitivity of the dominant lawn fluxes, and the fact that lawns are unlikely to dry out, climate warming may substantially increase CH4 emissions in northern Finland, and in aapa mire regions in general.This work was carried out within the Natural Environment Research Council (NERC) funded Arctic Biosphere Atmosphere Coupling at Multiple Scales (ABACUS) project (a contribution to International Polar Year 2007_2008) plus NERC small grant NE/F010222/1 awarded to RB and BH. We are grateful for the support of the staff at the Kevo Subarctic Research Institute, to David Sayer for operation and maintenance of the eddy covariance apparatus, and to Lorna English for helping with the analysis of the CH4 samples. We also thank the NERC Field Spectroscopy Facility for support in ground data collection for the remote sensing analysis. Finally, we wish to express our gratitude to two anonymous reviewers whose comments and suggestions substantially improved the manuscript

Detecting Human Presence and Influence on Neotropical Forests with Remote Sensing

The Amazon, and Neotropical forests, are one of the most important global biomes because of their extent and unique biodiversity, as well as their importance to global climate and as a habitat and resource for humans. Unravelling the influence of human presence on these forests is fundamental to our understanding of the biodiversity, ecosystem function, and service-providing potential. Human presence in these tropical rainforests dates back 13,000 years, and the impacts of this presence are hotly debated. Some authors suggest persistent effects of pre-Columbian plant domestication on current Amazonian forest composition. Other authors suggest that post-Columbian influence on forest composition is orders of magnitude higher than that of pre-Columbian times. Evidence from remote sensing has become increasingly useful as a way to help settle these debates. Here we review past, current, and future uses of remote sensing technology to detect human infrastructure in the Amazon and other Neotropical forests over the several historical periods of human presence, from archaeological to post-modern societies. We define human presence in terms of activities that left behind a footprint, such as settlements, earth-mounds, roads, use of timber and fuelwood, agriculture, soil, etc. Lastly, we discuss opportunities and challenges for the use of remote sensing to provide data and information necessary to expand our understanding of the history of human occupation in the Neotropical forests, and how this human occupation might affect biodiversity. There have been many recent applications of remote sensing to the detection of Pre-Columbian human infrastructure, from visual inspection of aerial photographs over deforested sites to uses of LiDAR on airborne and UAV platforms to detect infrastructure and smaller settlements under the canopy. Similar efforts are yet to be conducted for the Post-Columbian period, especially during the colonization and imperialism periods. Finally, our knowledge of human impacts in the modern era (20th and 21st centuries) is not-surprisingly more extensive. Remote sensing is still under-used and extremely useful for this type of application, and new missions might provide solutions that were unavailable before. Yet systematic ground surveys are irreplaceable, and detection accuracies of human presence from the combination of remote sensing and ground surveys need to be improved. It is vital therefore to understand how Neotropical forest biodiversity has developed in the presence of people in the past, the implications of this for predicting future directions of change in the Amazon and elsewhere

New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species Author links open overlay panel

Leaf angle distribution (LAD) is an important property which influences the spectral reflectance and radiation transmission properties of vegetation canopies, and hence interception, absorption and photosynthesis. It is a fundamental parameter of radiative transfer models of vegetation at all scales. Yet, the difficulty in measuring LAD causes it to be also one of the most poorly characterized parameters, and is typically either assumed to be random, or to follow one of a very small number of parametric ‘archetype’ functions. Terrestrial LiDAR scanning (TLS) is increasingly being used to measure canopy structure, but LAD estimation from TLS has been limited thus far. We introduce a fast and simple method for detection of LAD information from terrestrial LiDAR scanning (TLS) point clouds. Here, it is shown that LAD information can be obtained by simply accumulating all valid planes fitted to points in a leaf point cloud. As points alone do not have any normal vector, subsets of points around each point are used to calculate the normal vectors. Importantly, for the first time we demonstrate the effect of distance on the reliable LAD information retrieval with TLS data. We test, validate, and compare the TLS-based method with established leveled digital photography (LDP) approach. We do this using data from both real trees covering the full range of existing leaf angle distribution type, but also from 3D Monte Carlo ray tracing. Crucially, this latter approach allows us to simulate both images and TLS point clouds from the same trees, for which the LAD is known explicitly a priori. This avoids the difficulty of assessing LAD manually, which being a difficult and potentially error-prone process, is an additional source of error in assessing the accuracy of LAD extraction methods from TLS or photography. We show that compared to the LDP measurement technique, TLS is not limited by leaf curvature, and depending on the distance of the TLS from the target, is potentially capable of retrieving leaf angle information from more complex, non-flat leaf surfaces. We demonstrate the possible limitation of TLS measurement techniques for the retrieval of LAD information for more distant canopies, or for taller trees (h > 20 m)

To What Extent Can UAV Photogrammetry Replicate UAV LiDAR to Determine Forest Structure? A Test in Two Contrasting Tropical Forests

Tropical forests are complex multi-layered systems, with the height and three-dimensional (3D) structure of trees influencing the carbon and biodiversity they contain. Fine-resolution 3D data on forest structure can be collected reliably with Light Detection and Ranging (LiDAR) sensors mounted on aircraft or Unoccupied Aerial Vehicles (UAVs), however, they remain expensive to collect and process. Structure-from-Motion (SfM) Digital Aerial Photogrammetry (SfM-DAP), which relies on photographs taken of the same area from multiple angles, is a lower-cost alternative to LiDAR for generating 3D data on forest structure. Here, we evaluate how SfM-DAP compares to LiDAR data acquired concurrently using a fixed-wing UAV, over two contrasting tropical forests in Gabon and Peru. We show that SfM-DAP data cannot be used in isolation to measure key aspects of forest structure, including canopy height (%Bias: 40%–50%), fractional cover, and gap fraction, due to difficulties measuring ground elevation, even under low tree cover. However, we find even in complex forests, SfM-DAP is an effective means of measuring top-of-canopy structure, including surface heterogeneity, and is capable of producing similar measurements of vertical structure as LiDAR. Thus, in areas where ground height is known, SfM-DAP is an effective method for measuring important aspects of forest structure, including canopy height, and gaps, however, without ground data, SfM-DAP is of more limited utility. Our results support the growing evidence base pointing to photogrammetry as a viable complement, or alternative, to LiDAR, capable of providing much needed information to support the mapping and monitoring of biomass and biodiversity

Terrestrial laser scanning for plot-scale forest measurement

Plot-scale measurements have been the foundationfor forest surveys and reporting for over 200 years. Throughrecent 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 rangeof disciplines. We review the development of TLS as a plotscale measurement tool, including the evolution of both instrument hardware and key data processing methodologies.We highlight two broad data modelling approaches of gapprobability and geometrical modelling and the basic theorythat underpins these. Finally, we discuss the future prospects for increasing the utilisation of TLS for plot-scale forest assessment and forest monitoring

Consistent patterns of common species across tropical tree communities

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations 1–6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories 7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees