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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]

Accurate estimates of the total biomass in terrestrial vegetation are important for carbon dynamics studies at a variety of scales. Although aboveground biomass is difficult to quantify over large areas using traditional techniques, lidar remote sensing holds great promise for biomass estimation because it directly measures components of canopy structure such as canopy height and the vertical distribution of intercepted canopy surfaces. In this study, our primary goal was to explore the sensitivity of lidar to differences in canopy structure and aboveground biomass in a dense, neotropical rainforest. We first examined the relationship between simple vertical canopy profiles derived from field measurements and the estimated aboveground biomass (EAGB) across a range of field plots located in primary and secondary tropical rainforest and in agroforesty areas. We found that metrics from field-derived vertical canopy profiles are highly correlated (R2 up to .94) with EAGB across the entire range of conditions sampled. Next, we found that vertical canopy profiles from a large-footprint lidar instrument were closely related with coincident field profiles, and that metrics from both field and lidar profiles are highly correlated. As a result, metrics from lidar profiles are also highly correlated (R2 up to .94) with EAGB across this neotropical landscape. These results help to explain the nature of the relationship between lidar data and EAGB, and also lay the foundation to explore the generality of the relationship between vertical canopy profiles and biomass in other tropical regions. [Copyright &y& Elsevier]

Quantification of forest structure is important for developing a better understanding of how forest ecosystems function. Additionally, estimation of forest structural attributes, such as aboveground biomass (AGBM), is an important step in identifying the amount of carbon in terrestrial vegetation pools and is central to global carbon cycle studies. Although current remote sensing techniques recover such tropical forest structure poorly, new large-footprint lidar instruments show great promise. As part of a prelaunch validation plan for the Vegetation Canopy Lidar (VCL) mission, the Laser Vegetation Imaging Sensor (LVIS), a large-footprint airborne scanning lidar, was flown over the La Selva Biological Station, a tropical wet forest site in Costa Rica. The primary objective of this study was to test the ability of large-footprint lidar instruments to recover forest structural characteristics across a spectrum of land cover types from pasture to secondary and primary tropical forests. LVIS metrics were able to predict field-derived quadratic mean stem diameter (QMSD), basal area, and AGBM with R² values of up to .93, .72, and .93, respectively. These relationships were significant and nonasymptotic through the entire range of conditions sampled at the La Selva. Our results confirm the ability of large-footprint lidar instruments to estimate important structural attributes, including biomass in dense tropical forests, and when taken along with similar results from studies in temperate forests, strongly validate the VCL mission framework. [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]

Abstract: Foliage profiles retrieved from a scanning, terrestrial, near-infrared (1064nm), full-waveform lidar, the Echidna Validation Instrument (EVI), agree well with those obtained from an airborne, near-infrared, full-waveform, large footprint lidar, the Lidar Vegetation Imaging Sensor (LVIS). We conducted trials at 5 plots within a conifer stand at Sierra National Forest in August, 2008. Foliage profiles retrieved from these two lidar systems are closely correlated (e.g., r =0.987 at 100m horizontal distances) at large spatial coverage while they differ significantly at small spatial coverage, indicating the apparent scanning perspective effect on foliage profile retrievals. Also we noted the obvious effects of local topography on foliage profile retrievals, particularly on the topmost height retrievals. With a fine spatial resolution and a small beam size, terrestrial lidar systems complement the strengths of the airborne lidars by making a detailed characterization of the crowns from a small field site, and thereby serving as a validation tool and providing localized tuning information for future airborne and spaceborne lidar missions. [Copyright &y& Elsevier]

Three-dimensional (3-D) reconstructions of forest stands, constructed from scans of the Echidna® full-waveform terrestrial lidar, provide a new pathway to estimate forest structural parameters such as tree diameter at breast height, tree height, crown diameter, and stem count density (trees per hectare). We provide such reconstructions using data from the Echidna® Validation Instrument (EVI), which emits laser pulses at 1064nm wavelength and digitizes the full return waveform. We reconstructed four stands from the Sierra National Forest and two stands from Harvard Experimental Forest of 50m by 50m size, with varying tree density and species, using data acquired in 2008 and 2009. Our procedure processes each lidar pulse return to identify one or multiple “hits” and their associated peak return power; converts peak power to apparent reflectance; locates hits in Cartesian coordinate space and stores them as points in a point cloud with associated attributes; registers and merges five (Sierra) or nine (Harvard) overlapping scans into a single point cloud; identifies the ground plane and classifies ground hits; produces a local digital elevation model; classifies non-ground hits as trunk/branch or foliage hits using the relative width of the return pulse; and uses commercial software tools to display, manipulate, and interact with the point cloud to make direct measurements of trees in the virtual space of the reconstruction. Results show good to very good agreement between virtual and manual measurements of tree diameter, height, and crown size, with R 2 values ranging from 0.70 to 0.99. [ABSTRACT FROM AUTHOR]

Abstract: Research suggests that secondary forest structure and composition subsequent to human disturbance could be related to wind or vertebrate dependent seed dispersal mechanisms. This paper examines the capability of a waveform LiDAR system (LVIS: LiDAR Vegetation Imaging System) to detect the location of wind-dispersed or vertebrate-dispersed forest patches in a mosaic of a secondary Tropical Dry Forest in the Santa Rosa National Park, Guanacaste, Costa Rica. Canopy-height estimations derived from LVIS data were used to identify and locate two types of dispersal-dependent arrangements of canopy heights: flat-topped (windborne seed dispersed) or dome-shaped (vertebrate-dispersed) fragments. Following identification, a Ripley''s K indicator was used to compare the locations for each confirmed canopy type to those reported in existing maps. Results indicated that dome-shaped patches tended to be arranged in clusters at the middle of former pastures (far from older forests), and that flat-topped patches tended to be located adjacent to older forests, forming extensions from the old growth patches and at the downwind side of the slope. Such characteristics agree with the theoretical observation of the spatial configuration of vertebrate- and wind-dispersed fragments reported in the study site. Our findings demonstrate that processes controlling forest regeneration in the TDF can be successfully identified through LiDAR data. [Copyright &y& Elsevier]

Abstract: In 2005, hurricane Katrina resulted in a large disturbance to U.S. forests. Recent estimates of damage from hurricane Katrina have relied primarily on optical remote sensing and field data. This paper is the first large-scale study to use satellite-based lidar data to quantify changes in forest structure from that event. GLAS data for the years prior to and following hurricane Katrina were compared to wind speed, forest cover, and damage data to assess the adequacy of sensor sampling, and to estimate changes in Mean Canopy Height (MCH) over all areas that experienced tropical force winds and greater. Statistically significant decreases in MCH post-Katrina were found to increase with wind intensity: Tropical Storm ∆MCH=−0.5m, Category 1 ∆MCH=−2m, and Category 2 ∆MCH=−4m. A strong relationship was also found between changes in non-photosynthetic vegetation (∆NPV), a metric previously shown to be related to storm damage, and post-storm MCH. The season of data acquisition was shown to influence calculations of MCH and MCH loss, but did not preclude the detection of major large-scale patterns of damage. Results from this study show promise for using space-borne lidar for large-scale assessments of forest disturbance, and highlight the need for future data on vegetation structure from space. [ABSTRACT FROM AUTHOR]

It has been suggested that attempts to use remote sensing to map the spatial and structural patterns of individual tree species abundances in heterogeneous forests, such as those found in northeastern North America, may benefit from the integration of hyperspectral or multi-spectral information with other active sensor data such as lidar. Towards this end, we describe the integrated and individual capabilities of waveform lidar and hyperspectral data to estimate three common forest measurements – basal area (BA), above-ground biomass (AGBM) and quadratic mean stem diameter (QMSD) – in a northern temperate mixed conifer and deciduous forest. The use of this data to discriminate distribution and abundance patterns of five common and often, dominant tree species was also explored. Waveform lidar imagery was acquired in July 2003 over the 1000 ha. Bartlett Experimental Forest (BEF) in central New Hampshire (USA) using NASA''s airborne Laser Vegetation Imaging Sensor (LVIS). High spectral resolution imagery was likewise acquired in August 2003 using NASA''s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Field data (2001–2003) from over 400 US Forest Service Northern Research Station (USFS NRS) plots were used to determine actual site conditions. Results suggest that the integrated data sets of hyperspectral and waveform lidar provide improved outcomes over use of either data set alone in evaluating common forest metrics. Across all forest conditions, 8–9% more of the variation in AGBM, BA, and QMSD was explained by use of the integrated sensor data in comparison to either AVIRIS or LVIS metrics applied singly, with estimated error 5–8% lower for these variables. Notably, in an analysis using integrated data limited to unmanaged forest tracts, AGBM coefficients of determination improved by 25% or more, while corresponding error levels decreased by over 25%. When data were restricted based on the presence of individual tree species within plots, AVIRIS data alone best predicted species-specific patterns of abundance as determined by species fraction of biomass. Nonetheless, use of LVIS and AVIRIS data – in tandem – produced complementary maps of estimated abundance and structure for individual tree species, providing a promising adjunct to traditional forest inventory and conservation biology planning efforts. [Copyright &y& Elsevier]