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.     

Mapping canopy height using a combination of digital stereo‐photogrammetry and lidar

Ranging techniques such as lidar (LIght Detection And Ranging) and digital stereo‐photogrammetry show great promise for mapping forest canopy height. In this study, we combine these techniques to create hybrid photo‐lidar canopy height models (CHMs). First, photogrammetric digital surface models (DSMs) created using automated stereo‐matching were registered to corresponding lidar digital terrain models (DTMs). Photo‐lidar CHMs were then produced by subtracting the lidar DTM from the photogrammetric DSM. This approach opens up the possibility of retrospective mapping of forest structure using archived aerial photographs. The main objective of the study was to evaluate the accuracy of photo‐lidar CHMs by comparing them to reference lidar CHMs. The assessment revealed that stereo‐matching parameters and left–right image dissimilarities caused by sunlight and viewing geometry have a significant influence on the quality of the photo DSMs. Our study showed that photo‐lidar CHMs are well correlated to their lidar counterparts on a pixel‐wise basis (r up to 0.89 in the best stereo‐matching conditions), but have a lower resolution and accuracy. It also demonstrated that plot metrics extracted from the lidar and photo‐lidar CHMs, such as height at the 95th percentile of 20 m×20 m windows, are highly correlated (r up to 0.95 in general matching conditions).     

Characterizing forest canopy structure with lidar composite metrics and machine learning

A lack of reliable observations for canopy science research is being partly overcome by the gradual use of lidar remote sensing. This study aims to improve lidar-based canopy characterization with airborne laser scanners through the combined use of lidar composite metrics and machine learning models. Our so-called composite metrics comprise a relatively large number of lidar predictors that tend to retain as much information as possible when reducing raw lidar point clouds into a format suitable as inputs to predictive models of canopy structural variables. The information-rich property of such composite metrics is further complemented by machine learning, which offers an array of supervised learning models capable of relating canopy characteristics to high-dimensional lidar metrics via complex, potentially nonlinear functional relationships. Using coincident lidar and field data over an Eastern Texas forest in USA, we conducted a case study to demonstrate the ubiquitous power of the lidar composite metrics in predicting multiple forest attributes and also illustrated the use of two kernel machines, namely, support vector machine and Gaussian processes (GP). Results show that the two machine learning models in conjunction with the lidar composite metrics outperformed traditional approaches such as the maximum likelihood classifier and linear regression models. For example, the five-fold cross validation for GP regression models (vs. linear/log-linear models) yielded a root mean squared error of 1.06 (2.36) m for Lorey's height, 0.95 (3.43) m for dominant height, 5.34 (8.51) m²/ha for basal area, 21.4 (40.5) Mg/ha for aboveground biomass, 6.54 (9.88) Mg/ha for belowground biomass, 0.75 (2.76) m for canopy base height, 2.2 (2.76) m for canopy ceiling height, 0.015 (0.02) kg/m³ for canopy bulk density, 0.068 (0.133) kg/m² for available canopy fuel, and 0.33 (0.39) m²/m² for leaf area index. Moreover, uncertainty estimates from the GP regression were more indicative of the true errors in the predicted canopy variables than those from their linear counterparts. With the ever-increasing accessibility of multisource remote sensing data, we envision a concomitant expansion in the use of advanced statistical methods, such as machine learning, to explore the potentially complex relationships between canopy characteristics and remotely-sensed predictors, accompanied by a desideratum for improved error analysis.     

Scaling and assessment of GPP from MODIS using a combination of airborne lidar and eddy covariance measurements over jack pine forests

Understanding the influence of within-pixel land cover heterogeneity is essential for the extrapolation of measured and modeled CO₂ fluxes from the canopy to regional scales using remote sensing. Airborne light detection and ranging (lidar) was used to estimate spatial and temporal variations of gross primary production (GPP) across a jack pine chronosequence of four sites in Saskatchewan, Canada for comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product. This study utilizes high resolution canopy structural information obtained from airborne lidar to bridge gaps in spatial representation between plot, eddy covariance (EC), and MODIS estimates of vegetation GPP. First we investigate linkages between canopy structure obtained from measurements and light response curves at a jack pine chronosequence during the growing season of 2004. Second, we use the measured canopy height and foliage cover inputs to create a structure-based GPP model (GPP_Landsberg) which was tested in 2005. The GPP model is then run using lidar data (GPP_Lidar) and compared with eight-day cumulative MODIS GPP (GPP_MODIS) and EC observations (GPP_EC). Finally, we apply the lidar GPP model at spatial resolutions of 1 m to 1000 m to examine the influence of within-pixel heterogeneity and scaling (or pixel aggregation) on GPP_Lidar. When compared over eight-day cumulative periods throughout the 2005 growing season, the standard deviation of differences between GPP_lidar and GPP_MODIS were less than differences between either of them and GPP_EC at all sites. As might be expected, the differences between pixel aggregated GPP estimates are most pronounced at sites with the highest levels of spatial canopy heterogeneity. The results of this study demonstrate one method for using lidar to scale between eddy covariance flux towers and coarse resolution remote sensing pixels using a structure-based Landsberg light curve model.     

Impact of footprint diameter and off-nadir pointing on the precision of canopy height estimates from spaceborne lidar

A spaceborne lidar mission could serve multiple scientific purposes including remote sensing of ecosystem structure, carbon storage, terrestrial topography and ice sheet monitoring. The measurement requirements of these different goals will require compromises in sensor design. Footprint diameters that would be larger than optimal for vegetation studies have been proposed. Some spaceborne lidar mission designs include the possibility that a lidar sensor would share a platform with another sensor, which might require off-nadir pointing at angles of up to 16°. To resolve multiple mission goals and sensor requirements, detailed knowledge of the sensitivity of sensor performance to these aspects of mission design is required.This research used a radiative transfer model to investigate the sensitivity of forest height estimates to footprint diameter, off-nadir pointing and their interaction over a range of forest canopy properties. An individual-based forest model was used to simulate stands of mixed conifer forest in the Tahoe National Forest (Northern California, USA) and stands of deciduous forests in the Bartlett Experimental Forest (New Hampshire, USA). Waveforms were simulated for stands generated by a forest succession model using footprint diameters of 20 m to 70 m. Off-nadir angles of 0 to 16° were considered for a 25 m diameter footprint diameter.Footprint diameters in the range of 25 m to 30 m were optimal for estimates of maximum forest height (R² of 0.95 and RMSE of 3 m). As expected, the contribution of vegetation height to the vertical extent of the waveform decreased with larger footprints, while the contribution of terrain slope increased. Precision of estimates decreased with an increasing off-nadir pointing angle, but off-nadir pointing had less impact on height estimates in deciduous forests than in coniferous forests. When pointing off-nadir, the decrease in precision was dependent on local incidence angle (the angle between the off-nadir beam and a line normal to the terrain surface) which is dependent on the off-nadir pointing angle, terrain slope, and the difference between the laser pointing azimuth and terrain aspect; the effect was larger when the sensor was aligned with the terrain azimuth but when aspect and azimuth are opposed, there was virtually no effect on R² or RMSE. A second effect of off-nadir pointing is that the laser beam will intersect individual crowns and the canopy as a whole from a different angle which had a distinct effect on the precision of lidar estimates of height, decreasing R² and increasing RMSE, although the effect was most pronounced for coniferous crowns.     

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).     

Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data

The tundra-taiga transition zone stretches around the northern hemisphere separating boreal forest to the south from treeless tundra to the north. Tree cover and height are important variables to characterize this vegetation transition. Accurate continuous fields of tree cover and height would enable the delineation of the forest extent according to different criterion and provide useful data for change detection of this climatically sensitive ecotone. This study examined if multiangular remote sensing data has potential to improve the accuracy of the tree cover and height estimates in relation to nadir-view data. The satellite data consisted of Multi-angle Imaging SpectroRadiometer (MISR) data at 275 m and 1.1 km resolutions. The study area was located in the Fennoscandian tundra-taiga transition zone, in northernmost Finland. The continuous fields of tree cover and height were estimated using neural networks, which were trained and assessed by high-resolution biotope inventory data. The spectral-angular data together produced lower estimation errors than single band nadir, multispectral nadir or single band multiangular data alone. RMSE of the tree cover estimates reduced from 7.8% (relative RMSE 67.4%) to 6.5% (56.1%) at 275 m resolution, and from 5.4% (49.2%) to 4.1% (36.9%) at 1.1 km resolution, when multispectral nadir data were used together with multiangular data. RMSE of the tree height estimates reduced from 2.3 m (44.3%) to 2.0 m (37.6%) and from 1.8 m (35.4%) to 1.3 m (25.4%), respectively. The largest estimation errors occurred in mires and in areas of dense shrub cover, but the use of multiangular data also reduced estimation errors in these areas. The results suggest that directional information has potential to improve the tree cover and height estimates, and hence the accuracy of the land cover change detection in the tundra-taiga transition zone.     

Determining forest canopy characteristics using airborne laser data

A pulsed laser system was flown over a forested area in Pennsylvania which exhibited a wide range of canopy closure conditions. The lasing system acts as the ultraviolet light equivalent of radar, sensing not only the distance to the top of the forest canopy, but also the range to the forest floor. The data were analyzed to determine which components of the laser data could explain the variability in crown closure along the flight transect. Results indicated that canopy closure was most strongly related to the penetration capability of the laser pulse. Pulses were attenuated more quickly in a dense canopy. Hence the inability to find a strong ground return in the laser data after initially sensing the top of the canopy connoted dense canopy cover. Photogrammetrically acquired tree heights were compared to laser estimates; average heights differed by less than 1 m. The results indicated that the laser system may be used to remotely sense the vertical forest canopy profile. Elements of this profile are linearly related to crown closure and may be used to assess tree height.     

Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA

Lidar provides enhanced abilities to remotely map leaf area index (LAI) with improved accuracies. We aim to further explore the capability of discrete-return lidar for estimating LAI over a pine-dominated forest in East Texas, with a secondary goal to compare the lidar-derived LAI map and the GLOBCARBON moderate-resolution satellite LAI product. Specific problems we addressed include (1) evaluating the effects of analysts and algorithms on in-situ LAI estimates from hemispherical photographs (hemiphoto), (2) examining the effectiveness of various lidar metrics, including laser penetration, canopy height and foliage density metrics, to predict LAI, (3) assessing the utility of integrating Quickbird multispectral imagery with lidar for improving the LAI estimate accuracy, and (4) developing a scheme to co-register the lidar and satellite LAI maps and evaluating the consistency between them. Results show that the use of different analysts or algorithms in analyzing hemiphotos caused an average uncertainty of 0.35 in in-situ LAI, and that several laser penetration metrics in logarithm models were more effective than other lidar metrics, with the best one explaining 84% of the variation in the in-situ LAI (RMSE = 0.29 LAI). The selection of plot size and height threshold in calculating laser penetration metrics greatly affected the effectiveness of these metrics. The combined use of NDVI and lidar metrics did not significantly improve estimation over the use of lidar alone. We also found that mis-registration could induce a large artificial discrepancy into the pixelwise comparison between the coarse-resolution satellite and fine-resolution lidar-derived LAI maps. By compensating for a systematic sub-pixel shift error, the correlation between two maps increased from 0.08 to 0.85 for pines (n = 24 pixels). However, the absolute differences between the two LAI maps still remained large due to the inaccuracy in accounting for clumping effects. Overall, our findings imply that lidar offers a superior tool for mapping LAI at local to regional scales as compared to optical remote sensing, accuracies of lidar-estimate LAI are affected not only by the choice of models but also by the absolute accuracy of in-situ reference LAI used for model calibration, and lidar-derived LAI maps can serve as reliable references for validating moderate-resolution satellite LAI products over large areas.     

Spatially explicit characterization of boreal forest gap dynamics using multi-temporal lidar data

Understanding a disturbance regime such as gap dynamics requires that we study its spatial and temporal characteristics. However, it is still difficult to observe and measure canopy gaps extensively in both space and time using field measurements or bi-dimensional remote sensing images, particularly in open and patchy boreal forests. In this study, we investigated the feasibility of using small footprint lidar to map boreal canopy gaps of sizes ranging from a few square meters to several hectares. Two co-registered canopy height models (CHMs) of optimal resolution were created from lidar datasets acquired respectively in 1998 and 2003. Canopy gaps were automatically delineated using an object-based technique with an accuracy of 96%. Further, combinatorics was applied on the two CHMs and the delineated gaps to provide information on the area of old and new gaps, gap expansions, new random gap openings, gap closure due to lateral growth of adjacent vegetation or due to vertical growth of regeneration. The results obtained establish lidar as an excellent tool for rapidly acquiring detailed and spatially extensive short-term dynamics of canopy gaps.     

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.     

Filling invalid values in a lidar-derived canopy height model with morphological crown control

The light detection and ranging (lidar) technique has rapidly developed worldwide in numerous fields. The canopy height model (CHM), which can be generated from lidar data, is a useful model in forestry research. The CHM shows the canopy height above ground, and it indicates vertical elevation changes and the horizontal distribution of the canopy’s upper surface. Many vegetation parameters, which are important in forest inventory, can be extracted from the CHM. However, some abnormal or sudden changes of the height values (i.e. invalid values), which appear as unnatural holes in an image, exist in CHMs. This article proposes an approach to fill the invalid values in lidar-derived CHMs with morphological crown control. First, the Laplacian operator is applied to an original CHM to determine possible invalid values. Then, the morphological closing operator is applied to recover the crown coverage. By combining the two results, the possible invalid values in the CHM can be confirmed and replaced by corresponding values in the median-filtered CHM. The filling results from this new method are compared with those from other methods and with charge-coupled device images for evaluation. Finally, a CHM with random noise is used to test the filling correctness of the algorithm. The experiments show that this approach can fill the most invalid values well while refraining from overfilling.     

Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat-lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, “young” forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS-VCT disturbance products. The height of “young” forest is then mapped based on the derived relationships and the LTSS-VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS-VCT method yielded the lowest cross validation error. The R² and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97 m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS-VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5 m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed.     

Technical note: Canopy height models and airborne lasers to estimate forest biomass: Two problems

Three ground datasets were used to simulate the canopy height characteristics of tropical forests in Costa Rica for the purposes of forest biomass estimation. The canopy height models (CHMs) were used in conjunction with airborne laser data. Gross biomass estimation errors on the order of 50-90% arose in two of the three analyses. The characteristics of the datasets and the biomass estimation procedure are reviewed to identify sources of error. In one dataset, the width of the fixed-area ground plots were small enough (5 m) that significant portions of the overstory canopy above the plots were not accounted for in the ground samples. The use of mapped stand data from thin ground plots resulted in inaccurate CHMs, which in turn lead to gross overestimates of forest biomass (about 90% larger than the ground reference value). CHMs generated using mensuration data collected on thin, fixed-area plots may significantly underestimate the average canopy height and crown closure actually found on that plot. This underestimation problem is directly related to plot width. Below a critical threshold, the thinner the plot, the greater the underestimation bias. In the second dataset, it is believed that lower-than-normal rainfalls at the beginning and end of the wet season may have produced a forest canopy with reduced leaf area. The airborne laser pulses penetrated further into the canopy, resulting in airborne laser estimates of forest biomass which grossly underestimated reference values by about 50%. Changing canopy conditions (e.g. leaf loss due to drought, insect defoliation, storm damage) can affect the accuracy of a CHM. Sources of error are reported in order to forewarn those researchers who produce and utilize canopy height models.     

Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA

Habitat heterogeneity has long been recognized as a fundamental variable indicative of species diversity, in terms of both richness and abundance. Satellite remote sensing data sets can be useful for quantifying habitat heterogeneity across a range of spatial scales. Past remote sensing analyses of species diversity have largely been limited to correlative studies based on the use of vegetation indices or derived land cover maps. A relatively new form of laser remote sensing (lidar) provides another means to acquire information on habitat heterogeneity. Here we examine the efficacy of lidar metrics of canopy structural diversity as predictors of bird species richness in the temperate forests of Maryland, USA. Canopy height, topography and the vertical distribution of canopy elements were derived from lidar imagery of the Patuxent National Wildlife Refuge and compared to bird survey data collected at referenced grid locations. The canopy vertical distribution information was consistently found to be the strongest predictor of species richness, and this was predicted best when stratified into guilds dominated by forest, scrub, suburban and wetland species. Similar lidar variables were selected as primary predictors across guilds. Generalized linear and additive models, as well as binary hierarchical regression trees produced similar results. The lidar metrics were also consistently better predictors than traditional remotely sensed variables such as canopy cover, indicating that lidar provides a valuable resource for biodiversity research applications.     

Applicability of an automatic surface detection approach to micro-pulse photon-counting lidar altimetry data: implications for canopy height retrieval from future ICESat-2 data

We develop and validate an automated approach to determine canopy height, an important metric for global biomass assessments, from micro-pulse photon-counting lidar data collected over forested ecosystems. Such a lidar system is planned to be launched aboard the National Aeronautics and Space Administration’s follow-on Ice, Cloud and land Elevation Satellite mission (ICESat-2) in 2017. For algorithm development purposes in preparation for the mission, the ICESat-2 project team produced simulated ICESat-2 data sets from airborne observations of a commercial micro-pulse lidar instrument (developed by Sigma Space Corporation) over two forests in the eastern USA. The technique derived in this article is based on a multi-step mathematical and statistical signal extraction process which is applied to the simulated ICESat-2 data set. First, ground and canopy surfaces are approximately extracted using the statistical information derived from the histogram of elevations for accumulated photons in 100 footprints. Second, a signal probability metric is generated to help identify the location of ground, canopy-top, and volume-scattered photons. According to the signal probability metric, the ground surface is recovered by locating the lowermost high-photon density clusters in each simulated ICESat-2 footprint. Thereafter, canopy surface is retrieved by finding the elevation at which the 95th percentile of the above-ground photons exists. The remaining noise is reduced by cubic spline interpolation in an iterative manner. We validate the results of the analysis against the full-resolution airborne photon-counting lidar data, digital terrain models (DTMs), and canopy height models (CHMs) for the study areas. With ground surface residuals ranging from 0.2 to 0.5 m and canopy height residuals ranging from 1.6 to 2.2 m, our results indicate that the algorithm performs very well over forested ecosystems of canopy closure of as much as 80%. Given the method’s success in the challenging case of canopy height determination, it is readily applicable to retrieval of land ice and sea ice surfaces from micro-pulse lidar altimeter data. These results will advance data processing and analysis methods to help maximize the ability of the ICESat-2 mission to meet its science objectives.     

Analysis of a lidar voxel-derived vertical profile at the plot and individual tree scales for the estimation of forest canopy layer characteristics

The goal of the current study was to develop methods of estimating the height of vertical components within plantation coniferous forest using airborne discrete multiple return lidar. In the summer of 2008, airborne lidar and field data were acquired for Loblolly pine forest locations in North Carolina and Virginia, USA, which comprised a variety of stand conditions (e.g. stand age, nutrient regime, and stem density). The methods here implement both field plot-scale analysis and an automated approach for the delineation of individual tree crown (ITC) locations and horizontal extents through a marker-based region growing process applied to a lidar derived canopy height model. The estimation of vertical features was accomplished through aggregating lidar return height measurements into vertical height bins, of a given horizontal extent (plot or ITC), creating a vertical ‘stack’ of bins describing the frequency of returns by height. Once height bins were created the resulting vertical distributions were smoothed with a regression curve-line function and canopy layers were identified through the detection of local maxima and minima. Estimates from Lorey’s mean canopy height was estimated from plot-level curve-fitting with an overall accuracy of 5.9% coefficient of variation (CV) and the coefficient of determination (R²) value of 0.93. Estimates of height to the living canopy produced an overall R² value of 0.91 (11.0% CV). The presence of vertical features within the sub-canopy component of the fitted vertical function also corresponded to areas of known understory presence and absence. Estimates from ITC data were averaged to the plot level. Estimates of field Lorey’s mean canopy top height from average ITC data produced an R² value of 0.96 (7.9% CV). Average ITC estimates of height to the living canopy produced the closest correspondence to the field data, producing an R² value of 0.97 (6.2% CV). These results were similar to estimates produced by a statistical regression method, where R² values were 0.99 (2.4% CV) and 0.98 (4.9% CV) for plot average top canopy height and height to the living canopy, respectively. These results indicate that the characteristics of the dominant canopy can be estimated accurately using airborne lidar without the development of regression models, in a variety of intensively managed coniferous stand conditions.     

The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest

Vegetation structure is an important factor that influences wildlife-habitat selection, reproduction, and survival. However, field-based measurements of vegetation structure can be time consuming, costly, and difficult to undertake in areas that are remote and/or contain rough terrain. Light detection and ranging (lidar) is an active remote sensing technology that can quantify three-dimensional vegetation structure over large areas and thus holds promise for examining wildlife-habitat relationships. We used discrete-return airborne lidar data acquired over the Black Hills Experimental Forest in South Dakota, USA in combination with field-collected vegetation and bird data to assess the utility of lidar data in quantifying vegetation structural characteristics that relate to avian diversity, density, and occurrence. Indices of foliage height diversity calculated from lidar data were positively and significantly correlated with indices of bird species diversity, with the highest correlations observed when foliage height diversity categories contained proportionally more foliage layers near the forest floor (< 5 m). In addition, lidar-derived indices of vegetation volume were significantly correlated with bird density. Using lidar-derived vegetation height data in combination with multispectral IKONOS data, we delineated five general habitat types within the study area according to the presence of prominent vegetation layers at lower levels of the forest and predominant tree type (deciduous or conifer). Habitat type delineations were tested by examining the occurrence and relative density of two bird species common to the study area that prefer lower level vegetation for foraging and nesting. Dark-eyed Juncos were significantly associated with the 0.5–2.0 m high vegetation layer in pine-dominated stands, and Warbling Vireos were significantly associated with this same layer in aspen-dominated stands. These results demonstrate that discrete-return lidar can be an effective tool to remotely quantify vegetation structural attributes important to birds, and may be enhanced when used in combination with spectral data.     

基于无人机的喀斯特退化天坑地下森林树高特征研究

基于无人机提取喀斯特退化天坑地下森林的树高特征,探索乔木树高的生长策略与天坑局部圈闭化生境的关系,研究退化天坑作为物种避难所的价值。通过无人机遥感技术对退化天坑进行三维重建,提取退化天坑内外的树高信息。结果表明：退化天坑地下森林平均树高较地表高出约5 m。地下森林平均树高为10.47 m,地表平均树高为5.43 m,地表南坡平均树高为5.75 m,坑内树高的分布特征受海拔影响显著。在喀斯特天坑微生境的作用下,与坑外地表相比,坑内地下森林在树高方面具有显著的优势,光照是地下森林乔木类植物种内和种间竞争的主要因子,垂直梯度是退化天坑植被树高分布格局的首要特征。无人机遥感技术能够快速地获取退化天坑地下森林的树高信息,具有推广潜力。