Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.     

Testing for similarity in area‐based spatial patterns: Alternative methods to Andresen's spatial point pattern test

Andresen's spatial point pattern test (SPPT) compares two spatial point patterns on defined areal units; it identifies areas where the spatial point patterns diverge and aggregates these local (dis)similarities to one global measure. We discuss the limitations of the SPPT and provide two alternative methods to calculate differences in the point patterns. In the first approach we use differences in proportions tests corrected for multiple comparisons. We show how the size of differences matters, as with large point patterns many areas will be identified by SPPT as statistically different, even if those differences are substantively trivial. The second approach uses multinomial logistic regression, which can be extended to identify differences in proportions over continuous time. We demonstrate these methods by identifying areas where pedestrian stops by the New York City Police Department are different from violent crimes for 2006–2016.     

Estimating urban above ground biomass with multi-scale LiDAR

Background

Urban trees have long been valued for providing ecosystem services (mitigation of the “heat island” effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB.

Results

Camden has an estimated median AGB density of 51.6 Mg ha^–1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤?25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB.

Conclusion

Over the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.

     

Modeling the risk of spread and establishment for Asian longhorned beetle (Anoplophora glabripennis) in Massachusetts from 2008-2009

Land managers responsible for invasive species removal in the USA require tools to prevent the Asian longhorned beetle (Anoplophora glabripennis) (ALB) from decimating the maple-dominant hardwood forests of Massachusetts and New England. Species distribution models (SDMs) and spread models have been applied individually to predict the invasion distribution and rate of spread, but the combination of both models can increase the accuracy of predictions of species spread over time when habitat suitability is heterogeneous across landscapes. First, a SDM was fit to 2008 ALB presence-only locations. Then, a stratified spread model was generated to measure the probability of spread due to natural and human causes. Finally, the SDM and spread models were combined to evaluate the risk of ALB spread in Central Massachusetts in 2008–2009. The SDM predicted many urban locations in Central Massachusetts as having suitable environments for species establishment. The combined model shows the greatest risk of spread and establishment in suitable locations immediately surrounding the epicentre of the ALB outbreak in Northern Worcester with lower risk areas in suitable locations only accessible through long-range dispersal from access to human transportation networks. The risk map achieved an accuracy of 67% using 2009 ALB locations for model validation. This model framework can effectively provide risk managers with valuable information concerning the timing and spatial extent of spread/establishment risk of ALB and potential strategies needed for effective future risk management efforts.     

Use of cartographic images by expert and novice field geologists in planning fieldwork routes

Topographic maps and aerial photographs are particularly useful when geoscientists are faced with fieldwork tasks such as selecting paths for observation, establishing sampling schemes, or defining field regions. These types of images are crucial in bedrock geologic mapping, a cognitively complex field-based problem-solving task. Geologic mapping requires the geologist to correctly identify rock types and three-dimensional bedrock structures from often partial or poor-quality outcrop data while navigating through unfamiliar terrain. This paper compares the walked routes of novice to expert geologists working in the field (n = 66) with the results of a route planning and navigation survey of a similar population of geologists (n = 77). Results show clearly that those geologists with previous mapping experience make quick and decisive determinations about field areas from available imagery and maps, regardless of whether they are or not physically present in the field area. Recognition of geologic features enabled experts to form and verbalize a specific plan for travel through a landscape based on those features. Novices were less likely to develop specific travel route plans and were less likely to identify critical landscape cues from aerial photographs.     

A Framework and Software Tool to Support Collaborative Landscape Analysis: Fitting Square Pegs into Square Holes

For landscape models to be applied successfully in management situations, models must address appropriate questions, include relevant processes and interactions, be perceived as credible and involve people affected by decisions. We propose a framework for collaborative model building that can address these issues, and has its roots in adaptive management, computer‐supported collaborative work and landscape ecology. Models built through this framework integrate a variety of information sources, address relevant questions, and are customized for the particular landscape and policy environment under study. Participants are involved in the process from the start, and because their input is incorporated, they feel ownership of the resulting models, increasing the chance of model acceptance and application. There are two requirements for success: a tool that supports rapid model prototyping and modification, that makes a clear link between a conceptual and implemented model, and that has the ability to implement a wide range of model types; and a core team with skills in communication, research and analysis, and knowledge of ecology and forestry in addition to modelling. SELES (Spatially Explicit Landscape Event Simulator) is a tool for building and running models of landscape dynamics. It combines discrete event simulation with a spatial database and a relatively simple modelling language to allow rapid development of landscape simulations, and provides a high‐level means of specifying complex model behaviours ranging from management actions to natural disturbance and succession. We have applied our framework in several forest modelling projects in British Columbia, Canada. We have found that this framework increases the interest by local experts and decision‐makers to participate actively in the model building process. The workshop process and resulting models have efficiently provided insight into the dynamics of large landscapes over long time frames. The use of SELES has facilitated this process by providing a flexible, transparent environment in which models can be rapidly implemented and refined. As a result, model findings may be more readily incorporated into decision‐support systems designed to assist resource managers in making informed decisions.     

Comparative phylogeography of African rain forest trees: A review of genetic signatures of vegetation history in the Guineo-Congolian region

The biogeographic history of the African rain forests has been contentious. Phylogeography, the study of the geographic distribution of genetic lineages within species, can highlight the signatures of historical events affecting the demography and distribution of species (i.e. population fragmentation or size changes, range expansion/contraction) and, thereby, the ecosystems they belong to. The accumulation of recent data for African rain forests now enables a first biogeographic synthesis for the region. In this review, we explain which phylogeographic patterns are expected under different scenarios of past demographic change, and we give an overview of the patterns detected in African rain forest trees to discuss whether they support alternative hypotheses regarding the history of the African rain forest cover. The major genetic discontinuities in the region support the role of refugia during climatic oscillations, though not necessarily following the classically proposed scenarios. We identify in particular a genetic split between the North and the South of the Lower Guinean region. Finally we provide some perspectives for future study.     

Characterization of permineralized kerogen from an Eocene fossil fern

The processes of organic maturation that occur during the permineralization of fossils and the detailed chemistry of the resulting products are incompletely understood. Primary among such processes is the geochemical alteration of biological matter to produce kerogen, such as that which comprises the cell walls of the fossils studied here: essentially unmetamorphosed, Eocene plant axes (specimens of the fossil fern Dennstaedtiopsis aerenchymata cellularly permineralized in cherts of the Clarno Formation of Oregon and the Allenby Formation of British Columbia). The composition and molecular structure of the kerogen that comprises the cell walls of such axes were analyzed using ultraviolet Raman spectroscopy (UV–Raman), solid state ¹³C nuclear magnetic resonance spectroscopy (¹³C NMR) and pyrolysis–gas chromatography–mass spectrometry (py–GC–MS).Cellularly well-preserved fern axes from both geologic units exhibit similar overall molecular structure, being composed primarily of networks of aromatic rings and polyene chains that, unlike more mature kerogens, lack large polycyclic aromatic hydrocarbon (PAH) constituents. The cell walls of the Allenby Formation specimens are, however, less altered than those of the Clarno chert, exhibiting more prevalent oxygen-containing and alkyl functional groups and comprising a greater fraction of rock mass.The study represents the first demonstration of the effectiveness (and limitations) of the combined use of UV–Raman, ¹³C NMR and py–GC–MS for the analysis of the kerogenous cell walls of chert-permineralized vascular plants.