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• Old-growth definition criteria greatly impacts amount estimates and maps. • Physically-based old-growth definitions are detectable via lidar remote sensing. • Age-based old-growth definitions are difficult to detect using lidar remote sensing. • Age-based old-growth forests are extremely diverse in physical structure. Old-growth forests have been widely studied for decades. The extreme diversity of old forest characteristics has inspired an equally diverse set of old-growth definitions, and makes mapping old-growth difficult across large areas and different forest types. While the use of remote sensing in old-growth research is not new, there is a growing need for large scale mapping to improve understanding of old forest processes and to support old-growth conservation. Old-growth mapping requires definitions that are ecologically relevant to old forests while also transferable to remote sensing data. In this paper we develop a conceptual framework to evaluate three dimensions of old-growth—a temporal dimension related to tree ages, a physical dimension related to tree sizes, and a functional dimension related to forest processes. In the first part of our analysis, we classify forests throughout the eastern US as old or not with respect to each old-growth dimension using existing old-growth definitions and data from the US Forest Inventory and Analysis (FIA) program. We estimate the proportion of forest classified as old within a hexagon grid, resulting in a unique map of old forest proportion (OFP) for each dimension. Subsequently, we use spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) to reproduce each OFP map in a modeling framework designed to 1) assess the extent to which each dimension of forest oldness can be mapped at large spatial scales, and 2) identify biophysical GEDI variables related to each dimension of forest oldness. We estimate that only 2% of forest classified as old in any dimension satisfied the old criteria in all three dimensions. We found substantial spatial variation in the mapped OFP estimates across the three dimensions, highlighting how definition criteria impacts old-growth maps. We also found that physically old forests were more effectively mapped using GEDI data than functionally or temporally old forests, and that physically old forests were more structurally similar to one another than temporally or functionally old forests. Our modeling results indicate that while remote sensing may be best suited to mapping physical old-growth characteristics, definitions that rely solely on physical characteristics do not adequately represent old forests throughout the eastern US. We propose that future efforts to map old-growth with spaceborne remote sensing data may maximize utility through collaboration between western and indigenous old-growth experts to determine broad yet nuanced approaches that are appropriately tailored to the target variable of old forests. These efforts should balance explicit and ecologically relevant old-growth definitions specifically for mapping that can be linked to remotely sensed data, 2) appropriate spatial resolutions, and 3) flexible quantitative frameworks that encompass the complexities and heterogeneity of old forests. [ABSTRACT FROM AUTHOR]

NASA's Global Ecosystem Dynamics Investigation (GEDI), to be deployed on the International Space Station (ISS) in 2018, will provide billions of measurements of ground elevation and forest vertical structure. GEDI will acquire data only along transects or tracks, with between-track spacing of about 500 m. To fill the gaps in between these tracks and potentially produce higher spatial resolution products, appropriate fusion strategies between GEDI observations and other spatially contiguous datasets should be explored. One source of global data on canopy structure comes from the TanDEM-X (TDX) mission, which uses the technique of Interferometric Synthetic Aperture Radar (InSAR) to estimate surface structure. The goal of this paper is to explore the fusion of GEDI data with TDX for canopy height retrievals. In particular we examined the improvement in TDX height retrievals from a widely used scattering model – the Random Volume over Ground (RVoG) model using ancillary topographic data from simulated GEDI observations of surface elevation. Our study site is a mountainous, mixed-temperate forest: Hubbard Brook Experimental Forest (HBEF). We started with a wall-to-wall lidar data set acquired by the Land Vegetation and Ice Sensor (LVIS) that provides a close analogue to anticipated GEDI waveforms. We derived a reference canopy height map and a reference bare earth digital terrain model (DTM) using LVIS. We next simulated GEDI ground tracks over HBEF for the nominal one-year period and extracted these observations from the reference DTM. A series of experiments were then conducted to examine the impact of ancillary topographic information. Using two different sets of TDX acquisitions, we compared height from RVoG respectively using no external DTM, the full LVIS DTM, and the DTM derived from simulated GEDI data against reference canopy heights at 90 m spatial resolution. With no external DTM to remove the ground phase, the RVoG model estimated heights with the best RMSE error (of the two TDX acquisitions dates) of 4.3 m and a bias of 2.5 m. Using the full LVIS DTM, results improved to 3.5 m RMSE and a bias of 1.3 m. Using the simulated GEDI DTM, the RMSE was 4.6 m with a bias of 1.8 m. The agreement between predicted and actual heights was good, ranging from an r 2 of 0.39 (GEDI DTM) to 0.71 ( p -value < 0.0001 for all r 2 ) (full resolution DTM). We conclude that for our study area, TDX data used with the RVoG model was an effective means for estimating spatially continuous canopy structure. However, the large biases in height estimation require ancillary topographic data, such as those produced from GEDI, to reduce biases to acceptable levels. [ABSTRACT FROM AUTHOR]

Aim Understanding the drivers of forest structure, function and change is a fundamental problem in both theoretical ecology and applied forestry for carbon mapping and monitoring. An important component of forest ecology research often utilizes allometric equations to scale up local measurements to predict large-scale forest and ecosystem-level properties. However, both applied and theoretical allometries in forest ecology (such as metabolic scaling theory, MST) assume that many scaling relationships are insensitive to broad-scale climate gradients or species life histories. We aim to test these assumptions by mapping continental-scale forest allometry across environmental gradients in the United States. Location United States. Methods We fit exponents to two allometric relationships in c. 100,000 Forest Inventory Analysis (FIA) field plots: (1) the relationship between the height of an individual tree and its diameter, and (2) plot-level tree size distributions. We compare fitted exponents to environmental and life-history variables, such as climate, topography and forest structure, in an attempt to explain allometric variability and deviations from theoretically predicted allometries. Results We find that the structural allometry of forests varies strongly as a function of location in the United States. Allometric exponents appear to asymptote at approximately where MST theory predicts with increasing forest height, while deviations from MST are partially explained as a function of environmentally driven recruitment limitations and successional status. Main conclusions While we find support for invariant tree and stand allometric scaling relationships in forests that are in steady state with regard to demography and resources, we also find considerable spatial variability in forest allometric relationships when steady-state conditions are violated. These findings suggest that extensions of metabolic scaling theory should incorporate variation in demographic dynamics in younger successional forests, and factors influencing recruitment limitation. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar instrument that collects near-global measurements of forest structure. While expansive in scope, GEDI samples are spatially sparse and cover a small fraction of the land surface. Converting the sparse samples into spatially complete predictive maps is of practical importance for a number of ecological studies. A complicating factor is that GEDI collects measurements over forested and non-forested land alike, with no automatic labeling of the land type. Such classification is important, as it categorically influences the probability distribution of the spatial process and the ecological interpretation of the observations/predictions. We propose and implement a spatial mixture model, separating the observations and the greater spatial domain into two latent classes. The latent classes are governed by a Bernoulli spatial process, with spatial effects driven by a Gaussian process. Within each class, the process is governed by a separate spatial model, describing the unique probabilistic attributes. Model predictions take the form of scalar predictions of the GEDI observables as well as discrete labeling of the class membership. Inference is conducted through a Bayesian paradigm, yielding rich quantification of prediction and uncertainty through posterior predictive distributions. We demonstrate the method using GEDI data over Wollemi National Park, Australia, using optical data from Landsat 8 as model covariates. When compared to a single spatial model, the mixture model achieves much higher posterior predictive densities on the true value. When compared to a random forest model, a common algorithmic approach in the remote sensing community, the random forest achieves better absolute prediction accuracy for prediction locations far from observed training data locations, but at the expense of location-specific assessments of uncertainty. The unsupervised binary classifications of the mixture model appear broadly ecologically interpretable as forest and non-forest when compared to optical imagery, but further comparison to ground-truth data is required. [ABSTRACT FROM AUTHOR]

Shifts in species distributions are major fingerprint of climate change. Examining changes in species abundance structures at a continental scale enables robust evaluation of climate change influences, but few studies have conducted these evaluations due to limited data and methodological constraints. In this study, we estimate temporal changes in abundance from North American Breeding Bird Survey data at the scale of physiographic strata to examine the relative influence of different components of climatic factors and evaluate the hypothesis that shifting species distributions are multidirectional in resident bird species in North America. We quantify the direction and velocity of the abundance shifts of 57 permanent resident birds over 44 years using a centroid analysis. For species with significant abundance shifts in the centroid analysis, we conduct a more intensive correlative analysis to identify climate components most strongly associated with composite change of abundance within strata. Our analysis focus on two contrasts: the relative importance of climate extremes vs. averages, and of temperature vs. precipitation in strength of association with abundance change. Our study shows that 36 species had significant abundance shifts over the study period. The average velocity of the centroid is 5.89 km·yr−1. The shifted distance on average covers 259 km, 9% of range extent. Our results strongly suggest that the climate change fingerprint in studied avian distributions is multidirectional. Among 6 directions with significant abundance shifts, the northwestward shift was observed in the largest number of species ( n = 13). The temperature/average climate model consistently has greater predictive ability than the precipitation/extreme climate model in explaining strata-level abundance change. Our study shows heterogeneous avian responses to recent environmental changes. It highlights needs for more species-specific approaches to examine contributing factors to recent distributional changes and for comprehensive conservation planning for climate change adaptation. [ABSTRACT FROM AUTHOR]

The area of tropical secondary forests is increasing rapidly, but data on the physical and biological structure of the canopies of these forests are limited. To obtain such data and to measure the ontogeny of canopy structure during tropical rainforest succession, we studied patch-scale (5 m2) canopy structure in three areas of 18–36 year-old secondary forest in Costa Rica, and compared the results to data from old-growth forest at the same site. All stands were sampled with a stratified random design with complete harvest from ground level to the top of the canopy from a modular portable tower. All canopies were organized into distinct high- and low-leaf-density layers (strata), and multiple strata developed quickly with increasing patch height. The relation of total Leaf Area Index (LAI, leaf area per area of ground) to patch canopy height, the existence of distinct high and low leaf- density layers (strata and free air spaces), the depth and LAI of the canopy strata and free air spaces, and the relation of the number of strata to patch canopy height were remarkably constant across the entire successional gradient. Trees were the most important contributor to LAI at all stages, while contribution of palm LAI increased through succession. We hypothesize that canopy physical structure at the patch scale is driven by light competition and discuss how this hypothesis could be tested. That canopy physical structure was relatively independent of the identity of the species present suggests that canopy physical structure may be conserved even as canopy floristics shift due to changing climate. [ABSTRACT FROM AUTHOR]

The estimation and monitoring of 3D forest structure at large scales strongly rely on the use of remote sensing techniques. Today, two of them are able to provide 3D forest structure estimates: lidar and synthetic aperture radar (SAR) configurations. The differences in wavelength, imaging geometry, and technical implementation make the measurements provided by the two configurations different and, when it comes to the sensitivity to individual 3D forest structure components, complementary. Accordingly, the potential of combining lidar and SAR measurements toward an improved 3D forest structure estimation has been recognised from the very beginning. However, until today there is no established framework for this combination. This paper attempts to review differences, commonalities, and complementarities of lidar and SAR measurements. First, vertical lidar reflectance and SAR reflectivity profiles at different wavelengths are compared in different forest types. Then, current perspectives on their combination for the generation of enhanced structure products are discussed. Two promising frameworks for combining lidar and SAR measurements are reviewed. The first one is a model-based framework where lidar-derived parameters are used to initialize SAR scattering models, and relies on both the validity of the models and on the physical equivalence of the used lidar and SAR parameters. The second one is a structure-based framework based on the ability of lidar and SAR measurements to express physical forest structure by means of appropriate indices. These indices can then be used to establish a link between the two kind of measurements. The review is supported by experimental results achieved using space- and airborne data acquired in recent relevant mission and campaigns. [ABSTRACT FROM AUTHOR]

• We compared hybrid and hierarchical model-based (HMB) biomass predictors. • For small study areas, HMB performed best in terms of mean square error. • For large study areas, hybrid inference should be preferred. • The study was intended to provide guidelines for NASA's GEDI mission. NASA's Global Ecosystem Dynamics Investigation (GEDI) mission offers data for temperate and pan-tropical estimates of aboveground forest biomass (AGB). The spaceborne, full-waveform LiDAR from GEDI provides sample footprints of canopy structure, expected to cover about 4% of the land area following two years of operation. Several options are available for estimating AGB at different geographical scales. Using GEDI sample data alone, gridded biomass predictions are based on hybrid inference which correctly propagates errors due to the modeling and accounts for sampling variability, but this method requires at least two GEDI tracks in the area of interest. However, there are significant gaps in GEDI coverage and in some areas of interest GEDI data may need to be combined with other wall-to-wall remotely sensed (RS) data, such as those from multispectral or SAR sensors. In these cases, we may employ hierarchical model-based (HMB) inference that correctly considers the additional model errors that result from relating GEDI data to the wall-to-wall data. Where predictions are possible from both hybrid and HMB inference the question arises which framework to choose, and under what circumstances? In this paper, we make progress towards answering these questions by comparing the performance of the two prediction frameworks under conditions relevant for the GEDI mission. Conventional model-based (MB) inference with wall-to-wall TanDEM-X data was applied as a baseline prediction framework, which does not involve GEDI data at all. An important feature of the study was the comparison of AGB predictors in terms of both standard deviation (SD: the square root of variance) and root mean square error (RMSE: the square root of mean square error – MSE). Since, in model-based inference, the true AGB in an area of interest is a random variable, comparisons of the performance of prediction frameworks should preferably be made in terms of their RMSEs. However, in practice only the SD can be estimated based on empirical survey data, and thus it is important also to study whether or not the difference between the two uncertainty measures is small or large under conditions relevant for the GEDI mission. Our main findings were that: (i) hybrid and HMB prediction typically resulted in smaller RMSEs than conventional MB prediction although the difference between the three frameworks in terms of SD often was small; (ii) in most cases the difference between hybrid and HMB inference was small in terms of both RMSE and SD; (iii) the RMSEs for all frameworks was substantially larger than the SDs in small study areas whereas the two uncertainty measures were similar in large study areas, and; (iv) spatial autocorrelation of model residual errors had a large effect on the RMSEs of AGB predictors, especially in small study areas. We conclude that hybrid inference is suitable in most GEDI applications for AGB assessment, due to its simplicity compared to HMB inference. However, where GEDI data are sparse HMB inference should be preferred. [ABSTRACT FROM AUTHOR]