Airborne Discrete-return LiDAR Data Research Articles

Structure metrics to generalize biomass estimation from lidar across forest types from different continents

Forest aboveground biomass is a key variable in remote sensing based forest monitoring. Active sensor systems, such as lidar, can generate detailed canopy height products. Relationships between canopy height and biomass are commonly established via regression analysis using information from ground-truth plots. In this way, many site-specific height-biomass relationships have been proposed in the literature and applied for mapping in regional contexts. However, such relationships are only valid within the specific forest type for which they were calibrated. A generalized relationship would facilitate biomass estimation across forest types and regions. In this study, a combination of lidar-derived and ancillary structural descriptors is proposed as an approach for generalization between forest types. Each descriptor is supposed to quantify a different aspect of forest structure, i.e., mean canopy height, maximum canopy height, maximum stand density, vertical heterogeneity and wood density. Airborne discrete return lidar data covering 194 ha of forest inventory plots from five different sites including temperate and tropical forests from Africa, Europe, North, Central and South America was used. Biomass predictions using the best general model (nRMSE = 12.4%, R2 = 0.74) were found to be almost as accurate as predictions using five site-specific models (nRMSE = 11.6%, R2 = 0.78). The results further allow interpretation about the importance of the employed structure descriptors in the biomass estimation and the mechanisms behind the relationships. Understanding the relationship between canopy structure and aboveground biomass and being able to generalize it across forest types are important steps towards consistent large scale biomass mapping and monitoring using airborne and potentially also spaceborne platforms.

A Continuous Wavelet Transform Based Method for Ground Elevation Estimation Over Mountainous Vegetated Areas Using Satellite Laser Altimetry

Although a satellite laser altimetry Geoscience Laser Altimeter System (GLAS) can directly measure ground elevation, the measurement accuracy of ground elevation is generally limited over mountainous vegetated areas. Currently, most methods for ground elevation estimations that use GLAS data fail to obtain an accurate ground elevation in sloped areas. Therefore, this study aimed to propose a continuous wavelet transform (CWT) based method for better estimating ground elevation from GLAS data over mountainous vegetated areas. First, the CWT was applied to each GLAS waveform for peak detection. Second, all potential ground peaks were correctly identified using GLAS waveform parameters in conjunction with auxiliary digital elevation model data. Third, the true ground peak was determined from CWT results according to several strict rules, and then the ground elevation was calculated based on the position of the true ground peak. Finally, these ground elevation estimates were validated by the digital terrain model derived from airborne discrete-return LiDAR data in Genhe in the Neimeng province of China. Additionally, the CWT-based method was also compared with previous methods, which estimate ground elevation from GLAS data over mountainous vegetated areas based on a Gaussian decomposition. It was found that our CWT-based method can reduce the root-mean-square error of ground elevation estimates by up to 0.8 m. Overall, this study can provide an available solution for estimating ground elevation from large-footprint waveform LiDAR data over mountainous vegetated areas.

Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data.

The upcoming space-borne LiDAR satellite Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in 2018. Different from the waveform LiDAR system onboard the ICESat, ICESat-2 will use a micro-pulse photon-counting LiDAR system. Thus new data processing algorithms are required to retrieve vegetation canopy height from photon-counting LiDAR data. The objective of this paper is to develop and validate an automated approach for better estimating vegetation canopy height. The new proposed method consists of three key steps: 1) filtering out the noise photons by an effective noise removal algorithm based on localized statistical analysis; 2) separating ground returns from canopy returns using an iterative photon classification algorithm, and then determining ground surface; 3) generating canopy-top surface and calculating vegetation canopy height based on canopy-top and ground surfaces. This automatic vegetation height estimation approach was tested to the simulated ICESat-2 data produced from Sigma Space LiDAR data and Multiple Altimeter Beam Experimental LiDAR (MABEL) data, and the retrieved vegetation canopy heights were validated by canopy height models (CHMs) derived from airborne discrete-return LiDAR data. Results indicated that the estimated vegetation canopy heights have a relatively strong correlation with the reference vegetation heights derived from airborne discrete-return LiDAR data (R2 and RMSE values ranging from 0.639 to 0.810 and 4.08 m to 4.56 m respectively). This means our new proposed approach is appropriate for retrieving vegetation canopy height from micro-pulse photon-counting LiDAR data.

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R2(calibration) = 0.89, R2(validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha−1, and RMSE(validation) = 14.80 t·ha−1). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile

Abstract Biodiversity is considered to be an essential element of the Earth system, driving important ecosystem services. However, the conservation of biodiversity in a quickly changing world is a challenging task which requires cost-efficient and precise monitoring systems. In the present study, the suitability of airborne discrete-return LiDAR data for the mapping of vascular plant species richness within a Sub-Mediterranean second growth native forest ecosystem was examined. The vascular plant richness of four different layers (total, tree, shrub and herb richness) was modeled using twelve LiDAR-derived variables. As species richness values are typically count data, the corresponding asymmetry and heteroscedasticity in the error distribution has to be considered. In this context, we compared the suitability of random forest (RF) and a Generalized Linear Model (GLM) with a negative binomial error distribution. Both models were coupled with a feature selection approach to identify the most relevant LiDAR predictors and keep the models parsimonious. The results of RF and GLM agreed that the three most important predictors for all four layers were altitude above sea level, standard deviation of slope and mean canopy height. This was consistent with the preconception of LiDAR's suitability for estimating species richness, which is its capacity to capture three types of information: micro-topographical, macro-topographical and canopy structural. Generalized Linear Models showed higher performances (r2: 0.66, 0.50, 0.52, 0.50; nRMSE: 16.29%, 19.08%, 17.89%, 21.31% for total, tree, shrub and herb richness respectively) than RF (r2: 0.55, 0.33, 0.45, 0.46; nRMSE: 18.30%, 21.90%, 18.95%, 21.00% for total, tree, shrub and herb richness, respectively). Furthermore, the results of the best GLM were more parsimonious (three predictors) and less biased than the best RF models (twelve predictors). We think that this is due to the mentioned non-symmetric error distribution of the species richness values, which RF is unable to properly capture. From an ecological perspective, the predicted patterns agreed well with the known vegetation composition of the area. We found especially high species numbers at low elevations and along riversides. In these areas, overlapping distributions of thermopile sclerophyllos species, water demanding Valdivian evergreen species and species growing in Nothofagus obliqua forests occur. The three main conclusions of the study are: 1) appropriate model selection is crucial when working with biodiversity count data; 2) the application of RF for data with non-symmetric error distributions is questionable; and 3) structural and topographic information derived from LiDAR data is useful for predicting local plant species richness.

Individual snag detection using neighborhood attribute filtered airborne lidar data

The ability to estimate and monitor standing dead trees (snags) has been difficult due to their irregular and sparse distribution, often requiring intensive sampling methods to obtain statistically significant estimates. This study presents a new method for estimating and monitoring snags using neighborhood attribute filtered airborne discrete-return lidar data. The method first develops and then applies an automated filtering algorithm that utilizes three dimensional neighborhood lidar point-based intensity and density statistics to remove lidar points associated with live trees and retain lidar points associated with snags. A traditional airborne lidar individual-tree detection procedure is then applied to the snag-filtered lidar point cloud, resulting in stem map of identified snags with height estimates. The filtering algorithm was developed using training datasets comprised of four different forest types in wide range of stand conditions, and then applied to independent data to determine successful snag detection rates. Detection rates ranged from 43 to 100%, increasing as the size of snags increased. The overall detection rate for snags with DBH≥25cm was 56% (±2.9%) with low commission error rates. The method provides the ability to estimate snag density and stem map a large proportion of snags across the landscape. The resulting information can be used to analyze the spatial distribution of snags, provide a better understanding of wildlife snag use dynamics, assess achievement of stocking standard requirements, and bring more clarity to snag stocking standards.

Estimating FPAR of maize canopy using airborne discrete-return LiDAR data

The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter for ecosystem modeling, crop growth monitoring and yield prediction. Ground-based FPAR measurements are time consuming and labor intensive. Remote sensing provides an alternative method to obtain repeated, rapid and inexpensive estimates of FPAR over large areas. LiDAR is an active remote sensing technology and can be used to extract accurate canopy structure parameters. A method to estimating FPAR of maize from airborne discrete-return LiDAR data was developed and tested in this study. The raw LiDAR point clouds were processed to separate ground returns from vegetation returns using a filter method over a maize field in the Heihe River Basin, northwest China. The fractional cover (fCover) of maize canopy was computed using the ratio of canopy return counts or intensity sums to the total of returns or intensities. FPAR estimation models were established based on linear regression analysis between the LiDAR-derived fCover and the field-measured FPAR (R(2) = 0.90, RMSE = 0.032, p < 0.001). The reliability of the constructed regression model was assessed using the leave-one-out cross-validation procedure and results show that the regression model is not overfitting the data and has a good generalization capability. Finally, 15 independent field-measured FPARs were used to evaluate accuracy of the LiDAR-predicted FPARs and results show that the LiDAR-predicted FPAR has a high accuracy (R(2) = 0.89, RMSE = 0.034). In summary, this study suggests that the airborne discrete-return LiDAR data could be adopted to accurately estimate FPAR of maize.

Assessment of factors affecting shrub volume estimations using airborne discrete-return LiDAR data in Mediterranean areas

Shrub vegetation is a key element of Mediterranean forest areas and it is necessary to develop tools that allow a precise knowledge of this vegetation. This study aims to predict shrub volume and analyze the factors affecting the accuracy of these estimations in small stands using airborne discrete-return LiDAR data. The study was performed over 83 circular stands with 0.5 m radius located in Chiva (Spain) mainly occupied by Quercus coccifera. The vegetation inside each area was clear cut, and the height and the diameter of each plant was measured to compute the volume of shrub vegetation per stand. Volume values were related with maximum height values derived from LiDAR data reaching a coefficient of determination value R2 = 0.26. Afterwards, factors affecting the quality of volume estimations were analyzed, i.e., vegetation type, LiDAR density, and accuracy of the digital terrain model (DTM). Significant accuracy improvements (R2 = 0.71) were detected for stands with 0.5 m, LiDAR data density greater than 8 points/m2, vegetation Q. coccifera, and error associated to the DTM less than 0.20 m. These results show the feasibility of using LiDAR data to predict shrub volume under certain conditions, which can contribute to improved forest management and characterization.

Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index

Remote sensing of forest canopy cover has been widely studied recently, but little attention has been paid to the quality of field validation data. Ecological literature has two different coverage metrics. Vertical canopy cover (VCC) is the vertical projection of tree crowns ignoring within-crown gaps. Angular canopy closure (ACC) is the proportion of covered sky at some angular range around the zenith, and can be measured with a field-of-view instrument, such as a camera. We compared field-measured VCC and ACC at 15° and 75° from the zenith to different LiDAR (Light Detection and Ranging) metrics, using several LiDAR data sets and comprehensive field data. The VCC was estimated to a high precision using a simple proportion of canopy points in first-return data. Confining to a maximum 15° scan zenith angle, the absolute root mean squared error (RMSE) was 3.7–7.0%, with an overestimation of 3.1–4.6%. We showed that grid-based methods are capable of reducing the inherent overestimation of VCC. The low scan angles and low power settings that are typically applied in topographic LiDARs are not suitable for ACC estimation as they measure in wrong geometry and cannot easily detect small within-crown gaps. However, ACC at 0–15° zenith angles could be estimated from LiDAR data with sufficient precision, using also the last returns (RMSE 8.1–11.3%, bias –6.1–+4.6%). The dependency of LiDAR metrics and ACC at 0–75° zenith angles was nonlinear and was modeled from laser pulse proportions with nonlinear regression with a best-case standard error of 4.1%. We also estimated leaf area index from the LiDAR metrics with linear regression with a standard error of 0.38. The results show that correlations between airborne laser metrics and different canopy field characteristics are very high if the field measurements are done with equivalent accuracy.

Mapping of understory lichens with airborne discrete-return LiDAR data

High backscatter reflectance at NIR wavelengths has been observed for reindeer lichens ( Cladina sp.) in the laboratory. The results suggested that lichens could be separated from soil and other parts of forest understory using this property. An experiment was carried out to test this hypothesis in situ. The lichen vegetation of a 960-m 2 plot in a barren pine stand in Juupajoki, Finland was mapped in 3D, using methods of close-range photogrammetry. The data of two airborne discrete-return sensors were compared for their ability to classify understory lichen vegetation. Normalization of the LiDAR intensities was carried out, using natural targets. The results showed that lichen surfaces had a higher intensity than on average. Normalization of the intensities improved separability of lichens from other surfaces, and the best-case classification accuracy was 75%. Detailed analysis of geometric errors revealed small, decimeter-level planimetric offsets in the LiDAR datasets that affected the results notably.