LiDAR Height Metrics Research Articles

Mapping tropical forest aboveground biomass using airborne SAR tomography

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha−1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

Direct use of large-footprint lidar waveforms to estimate aboveground biomass

Many studies have established the strong connections between aboveground biomass and lidar height metrics; however, these relationships are site-specific. Field data required to derive these relationships are not readily available in many cases. We developed a model to estimate plot-level aboveground biomass density (AGBD) directly from large-footprint lidar waveform measurements. An individual tree-based aboveground biomass (AGB)-height allometric relationship was scaled up to the plot level using lidar-waveform sensed tree height and crown size distribution characteristics. The AGBD was estimated based on a waveform/foliage profile-weighted height-based allometric equation. The AGBD-height scaling exponent was then built on the allometric relationships of tree height with stem diameter and crown volume with tree height. Global vegetation structure data analysis demonstrated that one general model (scaling exponent ~ 1.6–1.8) works reasonably well across all global forest biomes except boreal forests (scaling exponent ~ 0.9). We applied the model to estimate aboveground biomass in two distinct geographic regions: temperate deciduous/conifer forests in the northeastern USA and a montane conifer forest in Sierra National Forest in California. Local vegetation structural data analysis leads to a consistent height scaling exponent for these two distinct biomes, slightly different from the global data analysis results. This model produced optimal AGBD estimates using the local height scaling exponent value. Adequate AGBD estimates with the general height scaling exponent value were also provided by our model. Our analysis suggests one general allometric relationship between plot-level AGBD and large-footprint lidar waveforms. Integrating local structure allometric relationships improve the predictive accuracy of the model. Our model outperformed the lidar height metrics-based approach for AGBD estimates and overcame the biomass underestimation problem using height metrics for high biomass regions. This model could potentially serve as a general and robust model for monitoring forest carbon stocks using large-footprint lidar waveform measurements such as the Global Ecosystem Dynamics Investigation (GEDI) mission at the continental and global scales. The model could be a framework for integrating a demography-based terrestrial ecosystem model and GEDI global mission measurements to improve global carbon stock and flux estimates.

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

ABSTRACTIn the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.

Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests

Synthetic Aperture Radar (SAR) backscatter measurements are sensitive to forest aboveground biomass (AGB), and the observations from space can be used for mapping AGB globally. However, the radar sensitivity saturates at higher AGB values depending on the wavelength and geometry of radar measurements, and is influenced by the structure of the forest and environmental conditions. Here, we examine the sensitivity of SAR at the L-band frequency (~25 cm wavelength) to AGB in order to examine the performance of future joint National Aeronautics and Space Administration, Indian Space Research Organisation NASA-ISRO SAR mission in mapping the AGB of global forests. For SAR data, we use the Phased Array L-Band SAR (PALSAR) backscatter from the Advanced Land Observing Satellite (ALOS) aggregated at a 100-m spatial resolution; and for AGB data, we use more than three million AGB values derived from the Geoscience Laser Altimeter System (GLAS) LiDAR height metrics at about 0.16–0.25 ha footprints across eleven different forest types globally. The results from statistical analysis show that, over all eleven forest types, saturation level of L-band radar at HV polarization on average remains ≥100 Mg·ha−1. Fresh water swamp forests have the lowest saturation with AGB at ~80 Mg·ha−1, while needleleaf forests have the highest saturation at ~250 Mg·ha−1. Swamp forests show a strong backscatter from the vegetation-surface specular reflection due to inundation that requires to be treated separately from those on terra firme. Our results demonstrate that L-Band backscatter relations to AGB can be significantly different depending on forest types and environmental effects, requiring multiple algorithms to map AGB from time series of satellite radar observations globally.

Characterizing forest structure variations across an intact tropical peat dome using field samplings and airborne LiDAR.

Tropical peat swamp forests (PSF) are one of the most carbon dense ecosystems on the globe and are experiencing substantial natural and anthropogenic disturbances. In this study, we combined direct field sampling and airborne LiDAR to empirically quantify forest structure and aboveground live biomass (AGB) across a large, intact tropical peat dome in Northwestern Borneo. Moving up a 4 m elevational gradient, we observed increasing stem density but decreasing canopy height, crown area, and crown roughness. These findings were consistent with hypotheses that nutrient and hydrological dynamics co-influence forest structure and stature of the canopy individuals, leading to reduced productivity towards the dome interior. Gap frequency as a function of gap size followed a power law distribution with a shape factor (λ) of 1.76 ± 0.06. Ground-based and dome-wide estimates of AGB were 217.7 ± 28.3 Mg C/ha and 222.4 ± 24.4 Mg C/ha, respectively, which were higher than previously reported AGB for PSF and tropical forests in general. However, dome-wide AGB estimates were based on height statistics, and we found the coefficient of variation on canopy height was only 0.08, three times less than stem diameter measurements, suggesting LiDAR height metrics may not be a robust predictor of AGB in tall tropical forests with dense canopies. Our structural characterization of this ecosystem advances the understanding of the ecology of intact tropical peat domes and factors that influence biomass density and landscape-scale spatial variation. This ecological understanding is essential to improve estimates of forest carbon density and its spatial distribution in PSF and to effectively model the effects of disturbance and deforestation in these carbon dense ecosystems.

Estimating plot volume using lidar height and intensity distributional parameters

This study explored the feasibility of height distributional metrics and intensity values extracted from low-density airborne light detection and ranging (lidar) data to estimate plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression analyses were performed using lidar height and intensity distributional metrics. The candidate variables for predicting plot volume were evaluated using three data sets: total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar returns used were corrected by the reference distance. Regression models were developed using each data set, and the first criterion used to select the best models was the corrected Akaike Information Criterion (AICc). The use of three data sets was statistically significant at R2 = 0.75 (RMSE = 52.17 m3 ha−1), R2 = 0.84 (RMSE = 45.24 m3 ha−1), and R2 = 0.91 (RMSE = 31.48 m3 ha−1) for total, canopy, and integrated lidar distributional metrics, respectively. Among the three data sets, the integrated lidar metrics-derived model showed the best performance for estimating plot volumes, improving errors up to 42% when compared to the other two data sets. This is attributed to supplementing variables weighted and biased to upper limits in dense plots with more statistical variables that explain the lower limits. In all data sets, intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard error were employed as explanatory variables. The use of intensity variables improved the accuracy of volume estimation in dense forests compared to prior research. Correction of the intensity values contributed up to a maximum of 58% improvement in volume estimation when compared to the use of uncorrected intensity values (R2 = 0.78, R2 = 0.53, and R2 = 0.63 for total, canopy, and integrated lidar distributional metrics, respectively). It is clear that the correction of intensity values is an essential step for the estimation of forest volume.

TerraSAR-X Stereo Radargrammetry and Airborne Scanning LiDAR Height Metrics in Imputation of Forest Aboveground Biomass and Stem Volume

Our objective is to evaluate the boreal forest aboveground biomass (AGB) and stem volume (VOL) imputation accuracy when scanning LiDAR or TerraSAR-X stereo radargrammetry-derived point-height metrics are used as predictors in the nearest neighbor imputation approach. Treewise measured field plots are used as reference data in the AGB and VOL imputations and accuracy evaluations. The digital terrain model (DTM) that is produced by the National Land Survey of Finland is used to obtain aboveground elevation values for the TerraSAR-X stereo radargrammetry. The DTM that is used (i.e., grid size 2 m) is derived from LiDAR surveys with an average point density of ~ 0.5 points/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The respective DTM and point data are used in LiDAR imputations of AGB and VOL. The relative root mean square errors (RMSEs) for AGB and VOL are 29.9% (41.3 t/ha) and 30.2% (78.1 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /ha) when using TerraSAR-X stereo radargrammetry metrics. The respective LiDAR estimation accuracy values are 21.9% (32.3 t/ha) and 24.8% (64.2 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /ha). LiDAR imputations are clearly more accurate than imputations that are made by using TerraSAR-X stereo radargrammetry metrics. However, the difference between imputation accuracies of LiDAR- and TerraSAR X-based features are smaller than in any previous study in which LiDAR and different types of synthetic aperture radar materials are compared in the variable predictions regarding forests. We conclude that TerraSAR X stereo radargrammetry is a promising remote-sensing technique for large forest-area AGB and VOL mapping and monitoring when an accurate LiDAR-based DTM is available.

Detecting tropical forest biomass dynamics from repeated airborne lidar measurements

Abstract. Reducing uncertainty of terrestrial carbon cycle depends strongly on the accurate estimation of changes of global forest carbon stock. However, this is a challenging problem from either ground surveys or remote sensing techniques in tropical forests. Here, we examine the feasibility of estimating changes of tropical forest biomass from two airborne lidar measurements of forest height acquired about 10 yr apart over Barro Colorado Island (BCI), Panama. We used the forest inventory data from the 50 ha Center for Tropical Forest Science (CTFS) plot collected every 5 yr during the study period to calibrate the estimation. We compared two approaches for detecting changes in forest aboveground biomass (AGB): (1) relating changes in lidar height metrics from two sensors directly to changes in ground-estimated biomass; and (2) estimating biomass from each lidar sensor and then computing changes in biomass from the difference of two biomass estimates, using two models, namely one model based on five relative height metrics and the other based only on mean canopy height (MCH). We performed the analysis at different spatial scales from 0.04 ha to 10 ha. Method (1) had large uncertainty in directly detecting biomass changes at scales smaller than 10 ha, but provided detailed information about changes of forest structure. The magnitude of error associated with both the mean biomass stock and mean biomass change declined with increasing spatial scales. Method (2) was accurate at the 1 ha scale to estimate AGB stocks (R2 = 0.7 and RMSEmean = 27.6 Mg ha−1). However, to predict biomass changes, errors became comparable to ground estimates only at a spatial scale of about 10 ha or more. Biomass changes were in the same direction at the spatial scale of 1 ha in 60 to 64% of the subplots, corresponding to p values of respectively 0.1 and 0.033. Large errors in estimating biomass changes from lidar data resulted from the uncertainty in detecting changes at 1 ha from ground census data, differences of approximately one year between the ground census and lidar measurements, and differences in sensor characteristics. Our results indicate that the 50 ha BCI plot lost a significant amount of biomass (−0.8 Mg ha−1 yr−1 ± 2.2(SD)) over the past decade (2000–2010). Over the entire island and during the same period, mean AGB change was 0.2 ± 2.4 Mg ha−1 yr−1 with old growth forests losing −0.7 Mg ha−1 yr−1 ± 2.2 (SD), and secondary forests gaining +1.8 Mg ha yr−1 ± 3.4 (SD) biomass. Our analysis suggests that repeated lidar surveys, despite taking measurement with different sensors, can estimate biomass changes in old-growth tropical forests at landscape scales (&gt;10 ha).

Remotely sensed indicators of forest conservation status: Case study from a Natura 2000 site in southern Portugal

The successful implementation of habitat conservation measures demands regular and spatially explicit monitoring and reporting on conservation status at a range of scales, based on indicators of both habitat range and condition (structure and functions required for long-term maintenance). Such is the case with the Natura 2000 protected areas in Europe. Focusing on the cork oak (Quercus suber) forests of one such area, the Serra de Monchique in southern Portugal, we test the complementarity and joint effectiveness of airborne multispectral and laser scanning (lidar) in providing robust indicators of conservation status. Principal forest types and other land covers are mapped to an accuracy of up to 70% (11 land cover classes) and 81% (5 classes) by fusing the two remote sensing datasets, results that are superior to using either one alone. Using previously tested relationships between lidar height metrics, forest vegetation structure and species diversity, we develop a map predicting areas of high (22% of area), medium (25%) and low (53%) condition. We recommend the further development and testing of remotely sensed range and condition indicators of conservation status for their application in important forested sites across Europe and beyond.

MAPPING UNDERSTORY TREES USING AIRBORNE DISCRETE-RETURN LIDAR DATA

Abstract. Understory trees in multi-layer stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement and biodiversity management. Cost-efficient inventory methods for the assessment of the presence, density, species- and size-distributions are called for. LiDAR remote sensing is a promising addition to field work. Unlike in passive image data, in which the signals from multiple layers mix, the 3D position of each hot-spot reflection is known in LiDAR data. The overstory however prevents from obtaining a wall-to-wall sample of understory, and measurements are subject to transmission losses. Discriminating between the crowns of dominant and suppressed trees can also be challenging. We examined the potential of LiDAR for the mapping of the understory trees in Scots pine stands (62°N, 24°E), using carefully georeferenced reference data and several LiDAR data sets. We present results that highlight differences in echo-triggering between sensors that affect the near-ground height data. A conceptual model for the transmission losses in the overstory was created and formulated into simple compensation models that reduced the intensity variation in second- and third return data. The task is highly ill-posed in discrete-return LiDAR data, and our models employed the geometry of the overstory as well as the intensity of previous returns. We showed that even first-return data in the understory is subject to losses in the overstory that did not trigger an echo. Even with compensation of the losses, the intensity data was deemed of low value in species discrimination. Area-based LiDAR height metrics that were derived from the data belonging to the crown volume of the understory showed reasonable correlation with the density and mean height of the understory trees. Assessment of the species seems out of reach in discrete-return LiDAR data, which is a drastic drawback.

Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation

Despite numerous studies existing for tree species classification the difficult situation in dense and mixed temperate forest is still a challenging task. This study attempts to extend the existing limitations by investigating comprehensive sets of different types of features derived from multiple data sources. These sets include features from full-waveform LiDAR, LiDAR height metrics, texture, hyperspectral data and colour infrared (CIR) images. Support vector machines (SVM) are used as an appropriate classifier to handle the high dimensional feature space and an internal ranking method allows the determination of the most important parameters. In addition, for species discrimination, focus is put on single tree applicable scale. While most experiences within these scales derive from boreal forests and are often restricted to two or three species, we concentrate on more complex temperate forests. The four main species pine (Pinus sylvestris), spruce (Picea abies), oak (Quercus petraea) and beech (Fagus sylvatica) are classified with an accuracy of 89.7%, 88.7%, 83.1% and 90.7%, respectively. Instead of directly classifying delineated single trees a raster cell based classification is conducted. This overcomes problems with erroneous polygons of merged tree crowns, which occur frequently within dense deciduous or mixed canopies. Lastly, we further test the possibility to correct these failures by combining species classification with single tree delineation.

Understory trees in airborne LiDAR data — Selective mapping due to transmission losses and echo-triggering mechanisms

Understory trees in multilayered stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement, and biodiversity management. Cost-efficient inventory methods are needed and airborne LiDAR is a promising addition to fieldwork. The overstory, however, obstructs wall-to-wall sampling of the understory using LiDAR, because transmission losses affect echo-triggering probabilities and intensity (peak amplitude) observations. We examined the potential of LiDAR in mapping of understory trees in pine (Pinus sylvestris L.) stands (62°N, 24°E), using careful experimentation. We formulated a conceptual model for the transmission losses and illustrated that loss normalization is highly ill-posed, especially for vegetation. The losses skew the population of targets that produce a subsequent echo. Losses up to 10–15% can occur even if an overstory echo is not triggered. In LiDAR sensors, quantized intensity values start from binary zero, but actually should include an offset, the noise level. We estimated these empirically. Constraining to low-loss pulses and ground data, we estimated parameters for compensation models that were based on the radar equation and employed the geometry of the pulse, as well as the overstory intensity observations as predictors. Intensity variation of second-return data was reduced, but, the intensity data were deemed of low value in species discrimination. Our results highlight differences between sensors in near-ground echo-triggering and height data. Area-based LiDAR height metrics from the understory had reasonable correlation with the density and mean height of the understory trees, whereas tree species seemed out of reach even if the transmission losses were compensated for. We conclude that transmission losses are a general impediment for radiometric analysis of multi-echo pulses in discrete-return and waveform LiDAR data.

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.

Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California

Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon sequestration in forests. We compare forest carbon estimates from Light Detection and Ranging (Lidar) data and QuickBird high-resolution satellite images, calibrated and validated by field measurements of individual trees. We conducted the tests at two sites in California: (1) 59 km2 of secondary and old-growth coast redwood (Sequoia sempervirens) forest (Garcia–Mailliard area) and (2) 58 km2 of old-growth Sierra Nevada forest (North Yuba area). Regression of aboveground live tree carbon density, calculated from field measurements, against Lidar height metrics and against QuickBird-derived tree crown diameter generated equations of carbon density as a function of the remote sensing parameters. Employing Monte Carlo methods, we quantified uncertainties of forest carbon estimates from uncertainties in field measurements, remote sensing accuracy, biomass regression equations, and spatial autocorrelation. Validation of QuickBird crown diameters against field measurements of the same trees showed significant correlation (r = 0.82, P < 0.05). Comparison of stand-level Lidar height metrics with field-derived Lorey's mean height showed significant correlation (Garcia–Mailliard r = 0.94, P < 0.0001; North Yuba R = 0.89, P < 0.0001). Field measurements of five aboveground carbon pools (live trees, dead trees, shrubs, coarse woody debris, and litter) yielded aboveground carbon densities (mean ± standard error without Monte Carlo) as high as 320 ± 35 Mg ha− 1 (old-growth coast redwood) and 510 ± 120 Mg ha− 1 (red fir [Abies magnifica] forest), as great or greater than tropical rainforest. Lidar and QuickBird detected aboveground carbon in live trees, 70–97% of the total. Large sample sizes in the Monte Carlo analyses of remote sensing data generated low estimates of uncertainty. Lidar showed lower uncertainty and higher accuracy than QuickBird, due to high correlation of biomass to height and undercounting of trees by the crown detection algorithm. Lidar achieved uncertainties of < 1%, providing estimates of aboveground live tree carbon density (mean ± 95% confidence interval with Monte Carlo) of 82 ± 0.7 Mg ha− 1 in Garcia–Mailliard and 140 ± 0.9 Mg ha− 1 in North Yuba. The method that we tested, combining field measurements, Lidar, and Monte Carlo, can produce robust wall-to-wall spatial data on forest carbon.

Aboveground large tree mass estimation in a coastal forest in British Columbia using plot-level metrics and individual tree detection from lidar

Plot-level large tree and snag aboveground mass (TSAM) in a second-growth coastal Douglas-fir forest stand in British Columbia was estimated using light detection and ranging (lidar) combining metrics from individually identified trees and snags and plot-level lidar canopy return density. Individual trees were identified using the tree variable window (TreeVaW) algorithm, which identifies tree crowns using a circular moving filter and the relationship between tree height and crown diameter. A multiple linear regression model was then developed to predict plot-level TSAM as determined from ground plots. The predicted heights of individually identified trees were very accurate (r2 = 0.92, SEE = 0.69 m). Plot TSAM was predicted with an r2 = 0.75 and SEE = 29.68 Mg/ha using lidar density and height metrics alone, and a slightly lower r2 = 0.71 and SEE = 31.95 Mg/ha using lidar density metrics with individually identified tree heights. Using individual tree metrics did not improve plot-level TSAM estimation, since a large component of TSAM is contained in complex canopy levels where individual trees are difficult to identify.