Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model

In order to prioritize the measurement requirements and accuracies of the two new lidar missions, a physical model is required for a fundamental understanding of the impact of surface topography, footprint size and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval. In this study, we extended a well developed Geometric Optical and Radiative Transfer (GORT) vegetation lidar model to take into account for the impacts of surface topography and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval and applied this extended model to assess the aforementioned impacts on vegetation lidar waveforms and height retrieval.Model simulation shows that surface topography and off-nadir pointing angle stretch waveforms and the stretching effect magnifies with footprint size, slope and off-nadir pointing angle. For an off-nadir pointing laser penetrating vegetation over a slope terrain, the waveform is either stretched or compressed based on the relative angle. The stretching effect also results in a disappearing ground peak return when slope or off-nadir pointing angle is larger than the “critical slope angle”, which is closely related to various vegetation structures and footprint size. Model simulation indicates that waveform shapes are affected by surface topography, off-nadir pointing angle and vegetation structure and it is difficult to remove topography effects from waveform extent based only on the shapes of waveform without knowing any surface topography information.Height error without correction of surface topography and off-nadir pointing angle is the smallest when the laser beams at the toward-slope direction and the largest from the opposite direction. Further simulation reveals within 20° of slope and off-nadir pointing angle, given the canopy height as roughly 25 m and the footprint size as 25 m, the error for vegetation height (RH100) ranges from − 2 m to greater than 12 m, and the error for the height at the medium energy return (RH50) from − 1 m to 4 m. The RH100 error caused by unknown surface topography and without correction of off-nadir pointing effect can be explained by an analytical formula as a function of vegetation height, surface topography, off-nadir pointing angle and footprint size as a first order approximation. RH50 is not much affected by topography, off-nadir pointing and footprint size. This forward model simulation can provide scientific guidance on prioritizing future lidar mission measurement requirements and accuracies.     

Physically based vertical vegetation structure retrieval from ICESat data: Validation using LVIS in White Mountain National Forest, New Hampshire, USA

Vegetation structure retrieval accuracies from spaceborne Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) data are affected by surface topography, background noise and sensor saturation. This study uses a physical approach to remove surface topography effect from lidar returns to retrieve vegetation height from ICESat/GLAS data over slope terrains. Slope-corrected vegetation heights from ICESat/GLAS data were compared to airborne Laser Vegetation Imaging Sensor (LVIS) (20 m footprint size) and small-footprint lidar data collected in White Mountain National Forest, NH. Impact of slope on LVIS vegetation height estimates was assessed by comparing LVIS height before and after slope correction with small-footprint discrete-return lidar and field data.Slope-corrected GLAS vegetation heights match well with 98 percentile heights from small-footprint lidar (R² = 0.77, RMSE = 2.2 m) and top three LVIS mean (slope-corrected) heights (R² = 0.64, RMSE = 3.7 m). Impact of slope on LVIS heights is small, however, comparison of LVIS heights (without slope correction) with either small footprint lidar or field data indicates that our scheme improves the overall LVIS height accuracy by 0.4-0.7 m in this region. Vegetation height can be overestimated by 3 m over a 15° slope without slope correction. More importantly, both slope-corrected GLAS and LVIS height differences are independent of slope. Our results demonstrate the effectiveness of the physical approach to remove surface topography from large footprint lidar data to improve accuracy of maximum vegetation height estimates.GLAS waveforms were compared to aggregated LVIS waveforms in Bartlett Experimental Forest, NH, to evaluate the impact of background noise and sensor saturation on vegetation structure retrievals from ICESat/GLAS. We found that GLAS waveforms with sensor saturation and low background noise match well with aggregated LVIS waveforms, indicating these waveforms capture vertical vegetation structure well. However, waveforms with large noise often lead to mismatched waveforms with LVIS and underestimation of waveform extent and vegetation height. These results demonstrate the quality of ICESat/GLAS vegetation structure estimates.     

Forest biomass mapping from lidar and radar synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed.In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R² of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and was used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R² of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar-SAR synergy was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.     

Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors

Tropical forests are an important component of the global carbon balance, yet there is considerable uncertainty in estimates of their carbon stocks and fluxes, which are typically estimated through analysis of aboveground biomass in field plots. Remote sensing technology is critical for assessing fine-scale spatial variability of tropical forest biomass over broad spatial extents. The goal of our study was to evaluate relatively new technology, small-footprint, discrete-return lidar and hyperspectral sensors, for the estimation of aboveground biomass in a Costa Rican tropical rain forest landscape. We derived a suite of predictive metrics for field plots: lidar metrics were calculated from plot vertical height profiles and hyperspectral metrics included fraction of spectral mixing endmembers and narrowband indices that respond to photosynthetic vegetation, structure, senescence, health and water and lignin content. We used single- and two-variable linear regression analyses to relate lidar and hyperspectral metrics to aboveground biomass of plantation, managed parkland and old-growth forest plots. The best model using all 83 biomass plots included two lidar metrics, plot-level mean height and maximum height, with an r² of 0.90 and root-mean-square error (RMSE) of 38.3 Mg/ha. When the analysis was constrained to plantation plots, which had the most accurate field data, the r² of the model increased to 0.96, with RMSE of 10.8 Mg/ha (n = 32). Hyperspectral metrics provided lower accuracy in estimating biomass than lidar metrics, and models with a single lidar and hyperspectral metric were no better than the best model using two lidar metrics. These results should be viewed as an initial assessment of using these combined sensors to estimate tropical forest biomass; hyperspectral data were reduced to nine indices and three spectral mixture fractions, lidar data were limited to first-return canopy height, sensors were flown only once at different seasons, and we explored only linear regression for modeling. However, this study does support conclusions from studies at this and other climate zones that lidar is a premier instrument for mapping biomass (i.e., carbon stocks) across broad spatial scales.     

Forest canopy height from the Multiangle Imaging SpectroRadiometer (MISR) assessed with high resolution discrete return lidar

In this study retrievals of forest canopy height were obtained through adjustment of a simple geometric-optical (GO) model against red band surface bidirectional reflectance estimates from NASA's Multiangle Imaging SpectroRadiometer (MISR), mapped to a 250 m grid. The soil-understory background contribution was partly isolated prior to inversion using regression relationships with the isotropic, geometric, and volume scattering kernel weights of a Li-Ross kernel-driven bidirectional reflectance distribution function (BRDF) model. The height retrievals were assessed using discrete return lidar data acquired over sites in Colorado as part of the Cold Land Processes Experiment (CLPX) and used with fractional crown cover retrievals to obtain aboveground woody biomass estimates. For all model runs with reasonable backgrounds and initial b/r (vertical to horizontal crown radii) values < 2.0, root mean square error (RMSE) distributions were centered between 2.5 and 3.7 m while R² distributions were centered between 0.4 and 0.7. The MISR/GO aboveground biomass estimates predicted via regression on fractional cover and mean canopy height for the CLPX sites showed good agreement with U.S. Forest Service Interior West map data (adjusted R² = 0.84). The implication is that multiangle sensors such as MISR can provide spatially contiguous retrievals of forest canopy height, cover, and aboveground woody biomass that are potentially useful in mapping distributions of aboveground carbon stocks, tracking disturbance, and in initializing, constraining, and validating ecosystem models. This is important because the MISR record is spatially comprehensive and extends back to the year 2000 and the launch of the NASA Earth Observing System (EOS) Terra satellite; it might thus provide a ~ 10-year baseline record that would enhance exploitation of data from the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission, as well as furthering realization of synergies with active instruments.     

Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level

The use of lidar remote sensing for mapping the spatial distribution of canopy characteristics has the potential to allow an accurate and efficient estimation of tree dimensions and canopy structural properties from local to regional and continental scales. The overall goal of this paper was to compare biomass estimates and height metrics obtained by processing GLAS waveform data and spatially coincident discrete-return airborne lidar data over forest conditions in east Texas. Since biomass estimates are derived from waveform height metrics, we also compared ground elevation measurements and canopy parameters. More specific objectives were to compare the following parameters derived from GLAS and airborne lidar: (1) ground elevations; (2) maximum canopy height; (3) average canopy height; (4) percentiles of canopy height; and (5) above ground biomass. We used the elliptical shape of GLAS footprints to extract canopy height metrics and biomass estimates derived from airborne lidar. Results indicated a very strong correlation for terrain elevations between GLAS and airborne lidar, with an r value of 0.98 and a root mean square error of 0.78 m. GLAS height variables were able to explain 80% of the variance associated with the reference biomass derived from airborne lidar, with an RMSE of 37.7 Mg/ha. Most of the models comparing GLAS and airborne lidar height metrics had R-square values above 0.9.     

Estimating forest canopy height and terrain relief from GLAS waveform metrics

Quantifying aboveground biomass in forest ecosystems is required for carbon stock estimation, aspects of forest management, and further developing a capacity for monitoring carbon stocks over time. Airborne Light Detection And Ranging (LiDAR) systems, of all remote sensing technologies, have been demonstrated to yield the most accurate estimates of aboveground biomass for forested areas over a wide range of biomass values. However, these systems are limited by considerations including large data volumes and high costs. Within the constraints imposed by the nature of the satellite mission, the GeoScience Laser Altimeter System (GLAS) aboard ICESat has provided data conferring information regarding forest vertical structure for large areas at a low end user cost. GLAS data have been demonstrated to accurately estimate forest height and aboveground biomass especially well in topographically smooth areas with homogeneous forested conditions. However in areas with dense forests, high relief, or heterogeneous vegetation cover, GLAS waveforms are more complex and difficult to consistently characterize. We use airborne discrete return LiDAR data to simulate GLAS waveforms and to subsequently deconstruct coregistered GLAS waveforms into vegetation and ground returns. A series of waveform metrics was calculated and compared to topography and vegetation information gleaned from the airborne data. A model to estimate maximum relief directly from waveform metrics was developed with an R² of 0.76 (n = 110), and used for the classification of the maximum relief of the areas sensed by GLAS. Discriminant analysis was also conducted as an alternative classification technique. A model was also developed estimating forest canopy height from waveform metrics for all of the data (R² = 0.81, n = 110) and for the three separate relief classes; maximum relief 0-7 m (R² = 0.83, n = 44), maximum relief 7-15 m (R² = 0.88, n = 41) and maximum relief > 15 m (R² = 0.75, n = 25). The moderate relief class model yielded better predictions of forest height than the low relief class model which is attributed to the increasing variability of waveform metrics with terrain relief. The moderate relief class model also yielded better predictions than the high relief class model because of the mixing of vegetation and terrain signals in waveforms from high relief footprints. This research demonstrates that terrain can be accurately modeled directly from GLAS waveforms enabling the inclusion of terrain relief, on a waveform specific basis, as supplemental model input to improve estimates of canopy height.     

Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment

Mean stand height is an important parameter for forest volume and biomass estimation in support of monitoring and management activities. Information on mean stand height is typically obtained through the manual interpretation of aerial photography, often supplemented by the collection of field calibration data. In remote areas where forest management practices may not be spatially exhaustive or where it is difficult to acquire aerial photography, alternate approaches for estimating stand height are required. One approach is to use very high spatial resolution (VHSR) satellite imagery (pixels sided less than 1 m) as a surrogate for air photos. In this research we demonstrate an approach for modelling mean stand height at four sites in the Yukon Territory, Canada, from QuickBird panchromatic imagery. An object-based approach was used to generate homogenous segments from the imagery (analogous to manually delineated forest stands) and an algorithm was used to automatically delineate individual tree crowns within the segments. A regression tree was used to predict mean stand height from stand-level metrics generated from the image grey-levels and within-stand objects relating individual tree crown characteristics. Heights were manually interpreted from the QuickBird imagery and divided into separate sets of calibration and validation data. The effects of calibration data set size and the input metrics used on the regression tree results were also assessed. The approach resulted in a model with a significant R² of 0.53 and an RMSE of 2.84 m. In addition, 84.6% of the stand height estimates were within the acceptable error for photo interpreted heights, as specified by the forest inventory standards of British Columbia. Furthermore, residual errors from the model were smallest for the stands that had larger mean heights (i.e., > 20 m), which aids in reducing error in subsequent estimates of biomass or volume (since stands with larger trees contribute more to overall estimates of volume or biomass). Estimated and manually interpreted heights were reclassified into 5-metre height classes (a schema frequently used for forest analysis and modelling applications) and compared; classes corresponded in 54% of stands assessed, and all stands had an estimated height class that was within ± 1 class of their actual class. This study demonstrates the capacity of VHSR panchromatic imagery (in this case QuickBird) for generating useful estimates of mean stand heights in unmonitored, remote, or inaccessible forest areas.     

An airborne lidar sampling strategy to model forest canopy height from Quickbird imagery and GEOBIA

High-resolution digital canopy models derived from airborne lidar data have the ability to provide detailed information on the vertical structure of forests. However, compared to satellite data of similar spatial resolution and extent, the small footprint airborne lidar data required to produce such models remain expensive. In an effort to reduce these costs, the primary objective of this paper is to develop an airborne lidar sampling strategy to model full-scene forest canopy height from optical imagery, lidar transects and Geographic Object-Based Image Analysis (GEOBIA). To achieve this goal, this research focuses on (i) determining appropriate lidar transect features (i.e., location, direction and extent) from an optical scene, (ii) developing a mechanism to model forest canopy height for the full-scene based on a minimum number of lidar transects, and (iii) defining an optimal mean object size (MOS) to accurately model the canopy composition and height distribution. Results show that (i) the transect locations derived from our optimal lidar transect selection algorithm accurately capture the canopy height variability of the entire study area; (ii) our canopy height estimation models have similar performance in two lidar transect directions (i.e., north-south and west-east); (iii) a small lidar extent (17.6% of total size) can achieve similar canopy height estimation accuracies as those modeled from the full lidar scene; and (iv) different MOS can lead to distinctly different canopy height results. By comparing the best canopy height estimate with the full lidar canopy height data, we obtained average estimation errors of 6.0 m and 6.8 m for conifer and deciduous forests at the individual tree crown/small tree cluster level, and an area weighted combined error of 6.2 m, which is lower than the provincial forest inventory height class interval (i.e., ≈ 9.0 m).     

Development of a simulation model to predict LiDAR interception in forested environments

Airborne scanning LiDAR systems are used to predict a range of forest attributes. However, the accuracy with which this can be achieved is highly dependent on the sensor configuration and the structural characteristics of the forest examined. As a result, there is a need to understand laser light interactions with forest canopies so that LiDAR sensor configurations can be optimised to assess particular forest types. Such optimisation will not only ensure the targeted forest attributes can be accurately and consistently quantified, but may also minimise the cost of data acquisition and indicate when a survey configuration will not deliver information needs.In this paper, we detail the development and application of a model to simulate laser interactions within forested environments. The developed model, known as the LiDAR Interception and Tree Environment (LITE) model, utilises a range of structural configurations to simulate trees with variable heights, crown dimensions and foliage clumping. We developed and validated the LITE model using field data obtained from three forested sites covering a range of structural classes. Model simulations were then compared to coincident airborne LiDAR data collected over the same sites. Results indicate that the LITE model can be used to produce comparable estimates of maximum height of trees within plots (differences < 2.42 m), mean heights of first return data (differences < 2.27 m), and canopy height percentiles (r² = 0.94, p < 0.001) when compared to airborne LiDAR data. In addition, the distribution of airborne LiDAR hits through the canopy profile was closely matched by model predictions across the range of sites. Importantly, this demonstrates that the structural differences between forest stands can be characterised by LITE. Models that are capable of interpreting the response of small-footprint LiDAR waveforms can facilitate algorithm development, the generation of corrections for actual LiDAR data, and the optimisation of sensor configurations for differing forest types, benefiting a range of experimental and commercial LiDAR applications. As a result, we also performed a scenario analysis to demonstrate how differences in forest structure, terrain, and sensor configuration can influence the interception of LiDAR beams.     

The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle

In response to the urgent need for improved mapping of global biomass and the lack of any current space systems capable of addressing this need, the BIOMASS mission was proposed to the European Space Agency for the third cycle of Earth Explorer Core missions and was selected for Feasibility Study (Phase A) in March 2009. The objectives of the mission are 1) to quantify the magnitude and distribution of forest biomass globally to improve resource assessment, carbon accounting and carbon models, and 2) to monitor and quantify changes in terrestrial forest biomass globally, on an annual basis or better, leading to improved estimates of terrestrial carbon sources (primarily from deforestation); and terrestrial carbon sinks due to forest regrowth and afforestation. These science objectives require the mission to measure above-ground forest biomass from 70° N to 56° S at spatial scale of 100-200 m, with error not exceeding ± 20% or ± 10 t ha^− 1 and forest height with error of ± 4 m. To meet the measurement requirements, the mission will carry a P-Band polarimetric SAR (centre frequency 435 MHz with 6 MHz bandwidth) with interferometric capability, operating in a dawn-dusk orbit with a constant incidence angle (in the range of 25°-35°) and a 25-45 day repeat cycle. During its 5-year lifetime, the mission will be capable of providing both direct measurements of biomass derived from intensity data and measurements of forest height derived from polarimetric interferometry. The design of the BIOMASS mission spins together two main observational strands: (1) the long heritage of airborne observations in tropical, temperate and boreal forest that have demonstrated the capabilities of P-band SAR for measuring forest biomass; (2) new developments in recovery of forest structure including forest height from Pol-InSAR, and, crucially, the resistance of P-band to temporal decorrelation, which makes this frequency uniquely suitable for biomass measurements with a single repeat-pass satellite. These two complementary measurement approaches are combined in the single BIOMASS sensor, and have the satisfying property that increasing biomass reduces the sensitivity of the former approach while increasing the sensitivity of the latter. This paper surveys the body of evidence built up over the last decade, from a wide range of airborne experiments, which illustrates the ability of such a sensor to provide the required measurements.At present, the BIOMASS P-band radar appears to be the only sensor capable of providing the necessary global knowledge about the world's forest biomass and its changes. In addition, this first chance to explore the Earth's environment with a long wavelength satellite SAR is expected to make yield new information in a range of geoscience areas, including subsurface structure in arid lands and polar ice, and forest inundation dynamics.     

Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape

Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m.A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 m tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r²=0.97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods.     

The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets

An evaluation of the use of airborne lidar for multi-temporal forest height growth assessment in a temperate mature red pine (Pinus resinosa Ait.) plantation over a five-year period is presented. The objective was to evaluate the level of uncertainty in lidar-based growth estimates through time so that the optimal repeat interval necessary for statistically meaningful growth measurements could be evaluated. Four airborne lidar datasets displaying similar survey configuration parameters were collected between 2000 and 2005. Coincident with the 2002 and 2005 acquisitions, field mensuration for 126 trees within 19 plots was carried out. Field measurements of stem height were compared to both coincident plot-level laser pulse return (LPR) height percentile metrics and stand level raster canopy height models (CHM).The average plot-level field heights were found to be 23.8 m (standard deviation (σ) = 0.4 m) for 2002 and 25.0 m (σ = 0.6 m) for 2005, with an approximate annual growth rate of 0.4 m/yr (σ = 0.5 m). The standard deviation uncertainty for field height growth estimates over the three year period was 41% at the plot-level (n = 19) and 92% at the individual tree level (n = 126). Of the lidar height percentile metrics tested, the 90th (L90), 95th (L95) and maximum (Lmax) LPR distribution heights demonstrated the highest overall correlations with field-measured tree height. While all lidar-based methods, including raster CHM comparison, tended to underestimate the field estimate of growth, Lmax provided the most robust overall direct estimate (0.32 m/yr, σ = 0.37 m). A single factor analysis of variance demonstrated that there was no statistically significant difference between all plot-level field and Lmax mean growth rate estimates (P = 0.38) and, further, that there was no difference in Lmax growth rate estimates across the examined time intervals (P = 0.59). A power function relationship between time interval and the standard deviation of error in growth estimate demonstrated that over a one-year period, the growth uncertainty was in the range of 0.3 m (∼ 100% of total growth) reducing to less than 0.1 m (∼ 6% of total growth) after 5 years. Assuming a 10% uncertainty is acceptable for operational or research-based conifer plantation growth estimates, this can be achieved at a three-year time interval.     

Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat

Remote sensing of forest vertical structure is possible with lidar data, but lidar is not widely available. Here we map tropical dry forest height (RMSE = 0.9 m, R² = 0.84, range 0.6-7 m), and we map foliage height profiles, with a time series of Landsat and Advanced Land Imager (ALI) imagery on the island of Eleuthera, The Bahamas, substituting time for vertical canopy space. We also simultaneously map forest disturbance type and age. We map these variables in the context of avian habitat studies, particularly for wintering habitat of an endangered Nearctic-Neotropical migrant bird, the Kirtland's Warbler (Dendroica kirtlandii). We also illustrate relationships between forest vertical structure, disturbance type and counts of forage species important to the Kirtland's Warbler. The ALI imagery and the Landsat time series are both critical to the result for forest height, which the strong relationship of forest height with disturbance type and age facilitates. Also unique to this study is that seven of the eight image time steps are cloud-cleared images: mosaics of the clear parts of several cloudy scenes. We created each cloud-cleared image, including a virtually seamless ALI image mosaic, with regression tree normalization. We also illustrate how viewing time series imagery as red-green-blue composites of tasseled cap wetness (RGB wetness composites) aids reference data collection for classifying tropical forest disturbance type and age. Our results strongly support current Landsat Program production of co-registered imagery, and they emphasize the value of seamless time series of cloud-cleared imagery.     

Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar

A ground-based, upward-scanning, near-infrared lidar, the Echidna® validation instrument (EVI), built by CSIRO Australia, retrieves forest stand structural parameters, including mean diameter at breast height (DBH), stem count density (stems/area), basal area, and above-ground woody biomass with very good accuracy in six New England hardwood and conifer forest stands. Comparing forest structural parameters retrieved using EVI data with extensive ground measurements, we found excellent agreement at the site level using five EVI scans (plots) per site (R² = 0.94-0.99); very good agreement at the plot level for stem count density and biomass (R² = 0.90-0.85); and good agreement at the plot level for mean DBH and basal area (R² = 0.48-0.66). The observed variance at site and plot levels suggest that a sample area of at least 1 ha (10⁴ m²) is required to estimate these parameters accurately at the stand level using either lidar-based or conventional methods. The algorithms and procedures used to retrieve these structural parameters are dependent on the unique ability of the Echidna® lidar to digitize the full waveform of the scattered lidar pulse as it returns to the instrument, which allows consistent separation of scattering by trunks and large branches from scattering by leaves. This successful application of ground-based lidar technology opens the door to rapid and accurate measurement of biomass and timber volume in areal sampling scenarios and as a calibration and validation tool for mapping biomass using airborne or spaceborne remotely sensed data.     

The use of waveform lidar to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire

The direct retrieval of canopy height and the estimation of aboveground biomass are two important measures of forest structure that can be quantified by airborne laser scanning at landscape scales. These and other metrics are central to studies attempting to quantify global carbon cycles and to improve understanding of the spatial variation in forest structure evident within differing biomes. Data acquired using NASA's Laser Vegetation Imaging Sensor (LVIS) over the Bartlett Experimental Forest (BEF) in central New Hampshire (USA) was used to assess the performance of waveform lidar in a northern temperate mixed conifer and deciduous forest.Using coincident plots established for this study, we found strong agreement between field and lidar measurements of height (r² = 0.80, p < 0.000) at the footprint level. Allometric calculations of aboveground biomass (AGBM) and LVIS metrics (AGBM: r² = 0.61, PRESS RMSE = 58.0 Mg ha^− 1, p < 0.000) and quadratic mean stem diameter (QMSD) and LVIS metrics (r² = 0.54, p = 0.002) also showed good agreement at the footprint level. Application of a generalized equation for determining AGBM proposed by Lefsky et al. (2002a) to footprint-level field data from Bartlett resulted in a coefficient of determination of 0.55; RMSE = 64.4 Mg ha^− 1; p = 0.002. This is slightly weaker than the strongest relationship found with the best-fit single term regression model.Relationships between a permanent grid of USDA Forest Service inventory plots and the mean values of aggregated LVIS metrics, however, were not as strong. This discrepancy suggests that validation efforts must be cautious in using pre-existing field data networks as a sole means of calibrating and verifying such remote sensing data. Stratification based on land-use or species composition, however, did provide the means to improve regression relationships at this scale. Regression models established at the footprint level for AGBM and QMSD were applied to LVIS data to generate predicted values for the whole of Bartlett. The accuracy of these models was assessed using varying subsets of the USFS NERS plot data. Coefficient of determinations ranged from fair to strong with aspects of land-use history and species composition influencing both the fit and the level of error seen in the predicted relationships.     

Exploring LiDAR-RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR

Scanning Light Detecting and Ranging (LiDAR), Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) were analyzed to determine (1) which of the three sensor systems most accurately predicted forest biomass, and (2) if LiDAR and SAR/InSAR data sets, jointly considered, produced more accurate, precise results relative to those same data sets considered separately. LiDAR ranging measurements, VHF-SAR cross-sectional returns, and X- and P-band cross-sectional returns and interferometric ranges were regressed with ground-estimated (from dbh) forest biomass in ponderosa pine forests in the southwestern United States. All models were cross-validated. Results indicated that the average canopy height measured by the scanning LiDAR produced the best predictive equation. The simple linear LiDAR equation explained 83% of the biomass variability (n = 52 plots) with a cross-validated root mean square error of 26.0 t/ha. Additional LiDAR metrics were not significant to the model. The GeoSAR P-band (λ = 86 cm) cross-sectional return and the GeoSAR/InSAR canopy height (X-P) captured 30% of the forest biomass variation with an average predictive error of 52.5 t/ha. A second RaDAR-FOPEN collected VHF (λ ∼ 7.8 m) and cross-polarized P-band (λ = 88 cm) cross-sectional returns, none of which proved useful for forest biomass estimation (cross-validated R² = 0.09, RMSE = 63.7 t/ha). Joint consideration of LiDAR and RaDAR measurements produced a statistically significant, albeit small improvement in biomass estimation precision. The cross-validated R² increased from 83% to 84% and the prediction error decreased from 26.0 t/ha to 24.9 t/ha when the GeoSAR X-P interferometric height is considered along with the average LiDAR canopy height. Inclusion of a third LiDAR metric, the 60th decile height, further increased the R² to 85% and decreased the RMSE to 24.1 t/ha. On this 11 km² ponderosa pine study area, LiDAR data proved most useful for predicting forest biomass. RaDAR ranging measurements did not improve the LiDAR estimates.     

Simulating the impact of discrete-return lidar system and survey characteristics over young conifer and broadleaf forests

We present a model-based investigation of the effect of discrete-return lidar system and survey characteristics on the signal recorded over young forest environments. A Monte Carlo ray tracing (MCRT) model of canopy scattering was used to examine the sensitivity of model estimates of lidar-derived canopy height, h_lidar to signal triggering method, canopy structure, footprint size, sampling density and scanning angle, for broadleaf and conifer canopies of varying density. Detailed 3D models of Scots pine (Pinus sylvestris) and Downy birch (Betula pubescens) were used to simulate lidar response, with minimal assumptions about canopy structure. Use of such models allowed the impact of lidar parameters on canopy height retrieval to be tested under a range of conditions typically not possible in practice. Retrieved h_lidar was generally found to be an underestimate of ‘true’ canopy height, h_canopy, but with exceptions. Choice of signal triggering method caused h_lidar to underestimate h_canopy by ∼ 4% for birch and ∼ 7% for pine (up to 66% in extreme cases). Variations in canopy structure resulted on average in underestimation of h_canopy by 13% for birch and between 29 and 48% for pine depending on age, but with over-estimates in some cases of up to 10%. Increasing footprint diameter from 0.1 to 1 m increased retrieved h_lidar from significant underestimates of h_canopy to values indistinguishable from h_canopy. Increased sampling density led to slightly increased values of h_lidar to close to h_canopy, but not significantly. Increasing scan angle increased h_lidar by up to 8% for birch, and 19% for pine at a scan angle of 30°. The impact of scan angle was greater for conifers as a result of large variation in crown height. Results showed that interactions between physically modelled (hypothetical) within canopy returns are similar to findings made in other studies using actual lidar systems, and that these modelled returns can depend strongly on the type of canopy and the lidar acquisition characteristics, as well as interactions between these properties. Physical models of laser pulse/canopy interactions may provide additional information on pulse interactions within the canopy, but require validation and testing before they are applied to actual survey planning and logistics.     

Discrete return lidar-based prediction of leaf area index in two conifer forests

Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify. Here we use lidar data in conjunction with SPOT5-derived spectral vegetation indices (SVIs) to examine the extent to which integration of both lidar and spectral datasets can estimate specific LAI quantities over a broad range of conifer forest stands in the northern Rocky Mountains. Our results show that SPOT5-derived SVIs performed poorly across our study areas, explaining less than 50% of variation in observed LAI, while lidar-only models account for a significant amount of variation across the two study areas located in northern Idaho; the St. Joe Woodlands (R² = 0.86; RMSE = 0.76) and the Nez Perce Reservation (R² = 0.69; RMSE = 0.61). Further, we found that LAI models derived from lidar metrics were only incrementally improved with the inclusion of SPOT 5-derived SVIs; increases in R² ranged from 0.02–0.04, though model RMSE values decreased for most models (0–11.76% decrease). Significant lidar-only models tended to utilize a common set of predictor variables such as canopy percentile heights and percentile height differences, percent canopy cover metrics, and covariates that described lidar height distributional parameters. All integrated lidar-SPOT 5 models included textural measures of the visible wavelengths (e.g. green and red reflectance). Due to the limited amount of LAI model improvement when adding SPOT 5 metrics to lidar data, we conclude that lidar data alone can provide superior estimates of LAI for our study areas.