Landscape-scale changes in forest canopy structure across a partially logged tropical peat swamp

Abstract. Forest canopy structure is strongly influenced by environmental factors and disturbance, and in turn influences key ecosystem processes including productivity, evapotranspiration and habitat availability. In tropical forests increasingly modified by human activities, the interplaying effects of environmental factors and disturbance legacies on forest canopy structure across landscapes are practically unexplored. We used high-fidelity airborne laser scanning (ALS) data to measure the canopy of old-growth and selectively logged peat swamp forest across a peat dome in Central Kalimantan, Indonesia, and quantified how canopy structure metrics varied with peat depth and under logging. Several million canopy gaps in different height cross-sections of the canopy were measured in 100 plots of 1 km2 spanning the peat dome, allowing us to describe canopy structure with seven metrics. Old-growth forest became shorter and had simpler vertical canopy profiles on deeper peat, consistently with previous work linking deep peat to stunted tree growth. Gap Size Frequency Distributions (GSFDs) indicated fewer and smaller canopy gaps on the deeper peat (i.e. the scaling exponent of pareto functions increased from 1.76 to 3.76 with peat depth). Areas subjected to concessionary logging until 2000, and informal logging since then, had the same canopy top height as old-growth forest, indicating the persistence of some large trees, but mean canopy height was significantly reduced; the total area of canopy gaps increased and the GSFD scaling exponent was reduced. Logging effects were most evident on the deepest peat, where nutrient depletion and waterlogged conditions restrain tree growth and recovery. A tight relationship exists between canopy structure and the peat deph gradient within the old-growth tropical peat swamp. This relationship breaks down after selective logging, with canopy structural recovery being modulated by environmental conditions. We are grateful to the Indonesia–Australia Forests and Carbon Partnership and (the no longer operating) Kalimantan Forests and Climate Partnership for sharing the ALS and peat depth data. This research was carried out in collaboration with the Governments of Australia and Indonesia, but the analysis and findings of this paper represent the views of the authors and do not necessarily represent the views of those Governments. We thank G. Vaglio Laurin for useful comments. We are grateful to A. Tanentzap for help with the RStan code and R. Kent and M. Dalponte for technical advice. B. M. M. Wedeux is funded by an AFR PhD Fellowship (1098188) from the Fonds National de la Recherche, Luxembourg.This is the final version of the article. It first appeared from the European Geosciences Union via http://dx.doi.org/10.5194/bgd-12-10985-201

Crown plasticity enables trees to optimize canopy packing in mixed-species forests

It has been suggested that diverse forests utilize canopy space more efficiently than species‐poor ones, as mixing species with complementary architectural and physiological traits allows trees to pack more densely. However, whether positive canopy packing–diversity relationships are a general feature of forests remains unclear. Using crown allometric data collected for 12 939 trees from permanent forest plots across Europe, we test (i) whether diversity promotes canopy packing across forest types and (ii) whether increased canopy packing occurs primarily through vertical stratification of tree crowns or as a result of intraspecific plasticity in crown morphology. We found that canopy packing efficiency increased markedly in response to species richness across a range of forest types and species combinations. Positive canopy packing–diversity relationships were primarily driven by the fact that trees growing in mixture had sizably larger crowns (38% on average) than those in monoculture. The ability of trees to plastically adapt the shape and size of their crowns in response to changes in local competitive environment is critical in allowing mixed‐species forests to optimize the use of canopy space. By promoting the development of denser and more structurally complex canopies, species mixing can strongly impact nutrient cycling and storage in forest ecosystems.The research leading to these results received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 265171.This is the accepted manuscript. The final version is available at http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12428/abstract

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

Understanding the causes of variation in plant functional traits is a central issue in ecology, particularly in the context of global change.  Spectroscopy is increasingly used for rapid and non-destructive estimation of foliar traits, but few studies have evaluated its accuracy when assessing phenotypic variation in multiple traits. Working with 24 chemical and physical leaf traits of six European tree species growing on strongly contrasting soil types (i.e. deep alluvium versus nearby shallow chalk), we asked (i) whether variability in leaf traits is greater between tree species or soil type; and (ii) whether field spectroscopy is effective at predicting intraspecific variation in leaf traits as well as interspecific differences.  Analysis of variance showed that inter-specific differences in traits were generally much stronger than intraspecific differences related to soil type, accounting for 25% versus 5% of total trait variation, respectively. Structural traits, phenolic defences and pigments were barely affected by soil type.  In contrast, foliar concentrations of rock-derived nutrients did vary: P and K concentration were lower on chalk than alluvial soils, while Ca, Mg, B, Mn and Zn concentrations were all higher, consistent with the findings of previous ecological studies. Foliar traits were predicted from 400-2500 nm reflectance spectra collected by field spectroscopy using partial least square regression, a method that is commonly employed in chemometrics.  Pigments were best modelled using reflectance data from the visible region (400 - 700 nm), whilst all other traits were best modelled using reflectance data from the shortwave infrared region (1100 - 2500 nm) region. Spectroscopy delivered accurate predictions of species-level variation in traits. However, it was ineffective at detecting intraspecific variation in rock-derived nutrients (with the notable exception of P). The explanation for this failure is that rock-derived elements do not have absorption features in the 400-2500 nm region, and their estimation is indirect, relying on elemental concentrations co-varying with structural traits that do have absorption features in that spectral region (“constellation effects”).  Since the structural traits did not vary with soil type, it was impossible for our regression models to predict intraspecific variation in rock-derived nutrients via constellation effects. This study demonstrates the value of spectroscopy for rapid, non-destructive estimation of foliar traits across species, but highlights problems with predicting intraspecific variation indirectly. We discuss the implications of these findings for mapping functional traits by airborne imaging spectroscopy.David Coomes was supported by a grant from NERC (NE/K016377/1) and Matheus H. Nunes is supported by a PhD scholarship from the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq)

Effect of Tree Phenology on LiDAR Measurement of Mediterranean Forest Structure

Retrieval of forest biophysical properties using airborne LiDAR is known to differ between leaf-on and leaf-off states of deciduous trees, but much less is understood about the within-season effects of leafing phenology. Here, we compare two LiDAR surveys separated by just six weeks in spring, in order to assess whether LiDAR variables were influenced by canopy changes in Mediterranean mixed-oak woodlands at this time of year. Maximum and, to a slightly lesser extent, mean heights were consistently measured, whether for the evergreen cork oak (Quercus suber) or semi-deciduous Algerian oak (Q. canariensis) woodlands. Estimates of the standard deviation and skewness of height differed more strongly, especially for Algerian oaks which experienced considerable leaf expansion in the time period covered. Our demonstration of which variables are more or less affected by spring-time leafing phenology has important implications for analyses of both canopy and sub-canopy vegetation layers from LiDAR surveys

Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning

Abstract. Borneo contains some of the world's most biodiverse and carbon-dense tropical forest, but this 750 000 km2 island has lost 62 % of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognizing the ecosystem services they provide, including their ability to store and sequester carbon. Airborne laser scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into statewide assessments of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and compositionally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rainforests of Sabah on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbon stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalized and effective approach for mapping forest carbon stocks in Borneo and underpins ongoing efforts to safeguard and facilitate the restoration of its unique tropical forests.This study was funded by the UK Natural Environment Research Council’s (NERC) Human Modified Tropical Forests research programme (grant numbers NE/K016377/1 and NE/K016407/1 awarded to the BALI and LOMBOK consortiums, respectively). We are grateful to NERC’s Airborne Research Facility and Data Analysis Node for conducting the survey and preprocessing the airborne data and to Abdullah Ghani for manning the GPS base station. David A. Coomes was supported in part by an International Academic Fellowship from the Leverhulme Trust. The Carnegie Airborne Observatory portion of the study was supported by the UN Development Programme, the Avatar Alliance Foundation, the Roundtable on Sustainable Palm Oil, the World Wildlife Fund and the Rainforest Trust. The Carnegie Airborne Observatory is made possible by grants and donations to Gregory P. Asner from the Avatar Alliance Foundation, the Margaret A. Cargill Foundation, the David and Lucile Packard Foundation, the Gordon and Betty Moore Foundation, the Grantham Foundation for the Protection of the Environment, the W. M. Keck Foundation, the John D. and Catherine T. MacArthur Foundation, the Andrew Mellon Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr., and William R. Hearst III. The SAFE project was supported by the Sime Darby Foundation.pData\Local\Programs\Python\Python36-32\python.exe %USERPROFILE%\Documents\GitHub\OATs\oasis.py %USERPROFILE%\AppData\Local\Programs\Python\Python35-32\python.exe %USERPROFILE%\OATS\oasis.p

Nonparametric image registration of airborne LiDAR, hyperspectral and photographic imagery of wooded landscapes

There is much current interest in using multisensor airborne remote sensing to monitor the structure and biodiversity of woodlands. This paper addresses the application of nonparametric (NP) image-registration techniques to precisely align images obtained from multisensor imaging, which is critical for the successful identification of individual trees using object recognition approaches. NP image registration, in particular, the technique of optimizing an objective function, containing similarity and regularization terms, provides a flexible approach for image registration. Here, we develop a NP registration approach, in which a normalized gradient field is used to quantify similarity, and curvature is used for regularization (NGF-Curv method). Using a survey of woodlands in southern Spain as an example, we show that NGF-Curv can be successful at fusing data sets when there is little prior knowledge about how the data sets are interrelated (i.e., in the absence of ground control points). The validity of NGF-Curv in airborne remote sensing is demonstrated by a series of experiments. We show that NGF-Curv is capable of aligning images precisely, making it a valuable component of algorithms designed to identify objects, such as trees, within multisensor data sets.This work was supported by the Airborne Research and Survey Facility of the U.K.’s Natural Environment Research Council (NERC) for collecting and preprocessing the data used in this research project [EU11/03/100], and by the grants supported from King Abdullah University of Science Technology and Wellcome Trust (BBSRC). D. Coomes was supported by a grant from NERC (NE/K016377/1) and funding from DEFRA and the BBSRC to develop methods for monitoring ash dieback from aircraft.This is the final version. It was first published by IEEE at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7116541&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_Publication_Number%3A36%29%26pageNumber%3D5

Taylor's law and related allometric power laws in New Zealand mountain beech forests: the roles of space, time and environment

This is the author accepted manuscript. The final version is available from Wiley via https://doi.org/10.1111/oik.02622Taylor's law says that the variance of population density of a species is proportional to a power of mean population density. Density–mass allometry says that mean population density is proportional to a power of mean biomass per individual. These power laws predict a third, variance–mass allometry: the variance of population density of a species is proportional to a power of mean biomass per individual. We tested these laws using 10 censuses of New Zealand mountain beech trees in 250 plots over 30 years at spatial scales from 5 m to kilometers. We found that: 1) a single-species forest not disrupted by humans obeyed all three laws; 2) random sampling explained the parameters of Taylor's law at a large spatial scale in 8 of 10 censuses, but not at a fine spatial scale; 3) larger spatial scale increased the exponent of Taylor's law and decreased the exponent of variance–mass allometry (this is the first empirical demonstration that the latter exponent depends on spatial scale), but affected the exponent of density–mass allometry slightly; 4) despite varying natural disturbance, the three laws varied relatively little over the 30 years; 5) self-thinning and recruiting plots had significantly different intercepts and slopes of density–mass allometry and variance–mass allometry, but the parameters of Taylor's law were not usually significantly affected; and 6) higher soil calcium was associated with higher variance of population density in all censuses but not with a difference in the exponent of Taylor's law, while elevation above sea level and soil carbon-to-nitrogen ratios had little effect on the parameters of Taylor's law. In general, the three laws were remarkably robust. When their parameters were influenced by spatial scale and environmental factors, the parameters could not be species-specific indicators. We suggest biological mechanisms that may explain some of these findings.JEC acknowledges U.S. National Science Foundation grant DMS-1225529 and the assistance of Priscilla K. Rogerson. RBA was supported by Landcare Research. This project benefited from many years of input by staff of the former New Zealand Forest Service, Forest and Range Experiment Station, Forest Research Institute and currently Landcare Research

Effects of plot size, stand density, and scan density on the relationship between airborne laser scanning metrics and the gini coefficient of tree size inequality

© 2017, Canadian Science Publishing. All rights reserved. Estimation of the Gini coefficient (GC) of tree sizes using airborne laser scanning (ALS) can provide maps of forest structure across the landscape, which can support sustainable forest management. A challenge arise s in determining the optimal spatial resolution that maximizes the stability and precision of GC estimates, which in turn depends on stand density or ALS scan density. By subsampling different plot sizes within large field plots, we evaluated the optimal spatial resolution by observing changes in GC estimation and in its correlation with ALS metrics. We found that plot size had greater effects than either stand density or ALS scan density on the relationship between GC and ALS metrics. Uncertainty in GC estimates fell as plot size increased. Correlation with ALS metrics showed convex curves with maxima at 250–450m 2 , which thus was considered the optimal plot size and, consequently, the optimal spatial resolution. By thinning the density of the ALS point cloud, we deduced that at least 3 points·m −2 were needed for reliable GC estimates. Many nationwide ALS scan densities are sparser than this, so may be unreliable for GC estimation. Ours is a simple approach for evaluating the optimal spatial resolution in remote sensing estimation of any forest attribute

An alternative approach to using LiDAR remote sensing data to predict stem diameter distributions across a temperate forest landscape

© 2017 by the authors. We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to the height distribution of first returns, we estimated parameters that succinctly describe SDDs that are most consistent with each 0.25-ha LiDAR tile across a 30,000 ha forest landscape. Tests with independent validation plots showed that the diameter distribution of stems was predicted with reasonable accuracy in most cases (half of validation plots had R2 ≥ 0.75, and another 23% had 0.5 ≤ R2 < 0.75). The predicted frequency of larger stems was much better than that of small stems (8 ≤ x < 11 cm diameter), particularly small conifers. We used the predicted SDDs to calculate aboveground carbon density (ACD; RMSE = 21.4 Mg C/ha), quadratic mean diameter (RMSE = 3.64 cm), basal area (RMSE = 6.99 m2/ha) and stem number (RMSE = 272 stems/ha). The accuracy of our predictions compared favorably with previous studies that have generally been undertaken in simpler conifer-dominated forest types. We demonstrate the utility of our results to spatial forest management planning by mapping SDDs, the proportion of broadleaves, and ACD at a 0.25 ha resolution

Airborne LiDAR detects selectively logged tropical forest even in an advanced stage of recovery

Identifying historical forest disturbances is difficult, especially in selectively logged areas. LiDAR is able to measure fine-scale variations in forest structure over multiple kilometers. We use LiDAR data from ca. 16 km2 of forest in Sierra Leone, West Africa, to discriminate areas of old-growth from areas recovering from selective logging for 23 years. We examined canopy height variation and gap size distributions. We found that though recovering blocks of forest differed little in height from old-growth forest (up to 3 m) they had a greater area of canopy gaps (average 10.2% gap fraction in logged areas, compared to 5.6% in unlogged area); and greater numbers of gaps penetrating to the forest floor (162 gaps at 2 m height in logged blocks, and 101 in an unlogged block). Comparison of LiDAR measurements with field data demonstrated that LiDAR delivered accurate results. We found that gap size distributions deviated from power-laws reported previously, with substantially fewer large gaps than predicted by power-law functions. Our analyses demonstrate that LiDAR is a useful tool for distinguishing structural differences between old-growth and old-secondary forests. That makes LiDAR a powerful tool for REDD+ (Reduction of Emissions from Deforestation and Forest Degradation) programs implementation and conservation planning.This research was funded by the European Union under the EuropeAid Programme, as a part of the Across the River Transboundary Peace Park Project DCI/ENV/2008/151-577; by a Cambridge Conservation Initiative Collaborative Fund grant “Applications of airborne remote sensing to the conservation management of a West African National Park”; and by the ERC grant Africa GHG #247349. We would also like to thank the British Technion Society for the generous funding of the post-doctoral Coleman-Cohen fellowship of R. Kent.This is the final version of the article. It first appeared from MDPI via http://dx.doi.org/10.3390/rs7070834