Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR

Airborne scanning LiDAR is a promising technique for efficient and accuratebiomass mapping due to its capacity for direct measurement of the three-dimensionalstructure of vegetation. A combination of individual tree detection (ITD) and an area-basedapproach (ABA) introduced in Vastaranta et al. [1] to map forest aboveground biomass(AGB) and stem volume (VOL) was investigated. The main objective of this study was totest the usability and accuracy of LiDAR in biomass mapping. The nearest neighbourmethod was used in the ABA imputations and the accuracy of the biomass estimation wasevaluated in the Finland, where single tree-level biomass models are available. The relativeroot-mean-squared errors (RMSEs) in plot-level AGB and VOL imputation were 24.9%and 26.4% when field measurements were used in training the ABA. When ITDmeasurements were used in training, the respective accuracies ranged between 28.5%–34.9%and 29.2%–34.0%. Overall, the results show that accurate plot-level AGB estimates can beachieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL wasencouraging when visually corrected ITD (ITDvisual) was used in training. We conclude that itis not feasible to use ITDvisual in wall-to-wall forest biomass inventory, but it could provide acost-efficient application for acquiring training data for ABA in forest biomass mapping.JRC.H.3-Forest Resources and Climat

Retrieval of forest aboveground biomass and volume with airbone scanning LiDAR

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Advances in measuring forest structure by terrestrial laser scanning with the Dual Wavelength ECHIDNA® LIDAR (DWEL)

Leaves in forests assimilate carbon from the atmosphere and woody components store the net production of that assimilation. Separate structure measurements of leaves and woody components advance the monitoring and modeling of forest ecosystem functions. This dissertation provides a method to determine, for the first time, the 3-D spatial arrangement and the amount of leafy and woody materials separately in a forest by classification of lidar returns from a new, innovative, lidar scanner, the Dual-Wavelength Echidna® Lidar (DWEL). The DWEL uses two lasers pulsing simultaneously and coaxially at near-infrared (1064 nm) and shortwave-infrared (1548 nm) wavelengths to locate scattering targets in 3-D space, associated with their reflectance at the two wavelengths. The instrument produces 3-D bispectral "clouds" of scattering points that reveal new details of forest structure and open doors to three-dimensional mapping of biophysical and biochemical properties of forests. The three parts of this dissertation concern calibration of bispectral lidar returns; retrieval of height profiles of leafy and woody materials within a forest canopy; and virtual reconstruction of forest trees from multiple scans to estimate their aboveground woody biomass. The test area was a midlatitude forest stand within the Harvard Forest, Petersham, Massachusetts, scanned at five locations in a 1-ha site in leaf-off and leaf-on conditions in 2014. The model for radiometric calibration assigned accurate values of spectral apparent reflectance, a range-independent and instrument-independent property, to scattering points derived from the scans. The classification of leafy and woody points, using both spectral and spatial context information, achieved an overall accuracy of 79±1% and 75±2% for leaf-off and leaf-on scans, respectively. Between-scan variation in leaf profiles was larger than wood profiles in leaf-off seasons but relatively similar to wood profiles in leaf-on seasons, reflecting the changing spatial heterogeneity within the stand over seasons. A 3-D structure-fitting algorithm estimated wood volume by modeling stems and branches from point clouds of five individual trees with cylinders. The algorithm showed the least variance for leaf-off, woody-points-only data, validating the value of separating leafy and woody points to the direct biomass estimates through the structure modeling of individual trees

Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps

Background: Estimation of forest biomass on the regional and global scale is of great importance. Many studies have demonstrated that lidar is an accurate tool for estimating forest aboveground biomass. However, results vary with forest types, terrain conditions and the quality of the lidar data. Methods: In this study, we investigated the utility of low density lidar data (<2 points∙m−2) for estimating forest aboveground biomass in the mountainous forests of northern Italy. As a study site we selected a 4 km2 area in the Valsassina mountains in Lombardy Region. The site is characterized by mixed and broad-leaved forests with variable stand densities and tree species compositions, being representative for the entire Pre-Alps region in terms of type of forest and geomorphology. We measured and determined tree height, DBH and tree species for 27 randomly located circular plots (radius =10 m) in May 2008. We used allometric equations to calculate total aboveground tree biomass and subsequently plot-level aboveground biomass (mg∙ha−1). Lidar data were collected in June 2004. Results: Our results indicate that low density lidar data can be used to estimate forest aboveground biomass with acceptable accuracies. The best height results show a R2 = 0.87 from final model and the root mean square error (RMSE) 1.02 m (8.3% of the mean). The best biomass model explained 59% of the variance in the field biomass. Leave-one-out cross validation yielded an RMSE of 30.6 mg∙ha−1 (20.9% of the mean). Conclusions: Low-density lidar data can be used to develop a forest aboveground biomass model from plot-level lidar height measurements with acceptable accuracies. In order to monitoring the National Forest Inventory, and respond to Kyoto protocol requirements, this analysis might be applied to a larger area. Keywords: LiDAR; Allometric equations; Plant height; Mixed fores

Leaf area index and aboveground biomass estimation of Populus and its hybrids using terrestrial LiDAR

Short rotation woody crops (SRWC) eastern cottonwood (Populus deltoides) and hybrid poplar plantations were established in 2021 in Pontotoc and Oktibbeha counties of Mississippi to study the biomass potential of SRWC for biofuel production. We used a novel backpack LiDAR system to measure forest metrics and harvested sample trees to build aboveground biomass (AGB) and leaf area index (LAI) equations. The results showed that LiDAR-derived variables accurately estimated aboveground biomass (R2 =0.81 and 29.22 % RMSE). However, the LAI estimation results showed that the LiDAR metrics moderately explained field measurements of LAI (R2 =0.31 and 18.05% RMSE) for individual-trees and poorly explained plot-level LAI measured with the LAI-2200C (R2 =0.11 and 66% RMSE). The backpack LiDAR system can be valuable for forest managers and researchers, enabling non-destructive AGB and LAI estimation. However, further research is required to overcome its limitations and achieve precise measurements of AGB and LAI

Estimation of Forest Biomass From Two-Level Model Inversion of Single-Pass InSAR Data

A model for aboveground biomass estimation from single-pass interferometric synthetic aperture radar (InSAR) data is presented. Forest height and canopy density estimates Delta h and eta(0), respectively, obtained from two-level model (TLM) inversion, are used as biomass predictors. Eighteen bistatic VV-polarized TanDEM-X (TDM) acquisitions are used, made over two Swedish test sites in the summers of 2011, 2012, and 2013 (nominal incidence angle: 41 degrees; height-of-ambiguity: 32-63 m). Remningstorp features a hemiboreal forest in southern Sweden, with flat topography and where 32 circular plots have been sampled between 2010 and 2011 (area: 0.5 ha; biomass: 42-242 t/ha; height: 14-32 m). Krycklan features a boreal forest in northern Sweden, 720-km north-northeast from Remningstorp, with significant topography and where 31 stands have been sampled in 2008 (area: 2.4-26.3 ha; biomass: 23-183 t/ha; height: 7-21 m). A high-resolution digital terrain model has been used as ground reference during InSAR processing. For the aforementioned plots and stands and if the same acquisition is used for model training and validation, the new model explains 65%-89% of the observed variance, with root-mean-square error (RMSE) of 12%-19% (median: 15%). By fixing two of the three model parameters, accurate biomass estimation can also be done when different acquisitions or different test sites are used for model training and validation, with RMSE of 12%-56% (median: 17%). Compared with a simple scaling model computing biomass from the phase center elevation above ground, the proposed model shows significantly better performance in Remningstorp, as it accounts for the large canopy density variations caused by active management. In Krycklan, the two models show similar performance

Regression-Based Retrieval of Boreal Forest Biomass in Sloping Terrain using P-band SAR Backscatter Intensity Data

A new biomass retrieval model for boreal forest using polarimetric P-band synthetic aperture radar (SAR) backscatter is presented. The model is based on two main SAR quantities: the HV backscatter and the HH/VV backscatter ratio. It also includes a topographic correction based on the ground slope. The model is developed from analysis of stand-wise data from two airborne P-band SAR campaigns: BioSAR 2007 (test site: Remningstorp, southern Sweden, biomass range: 10-287 tons/ha, slope range: 0-4 degrees) and BioSAR 2008 (test site: Krycklan, northern Sweden, biomass range: 8-257 tons/ha, slope range: 0-19 degrees). The new model is compared to five other models in a set of tests to evaluate its performance in different conditions. All models are first tested on data sets from Remningstorp with different moisture conditions, acquired during three periods in the spring of 2007. Thereafter, the models are tested in topographic terrain using SAR data acquired for different flight headings in Krycklan. The models are also evaluated across sites, i.e., training on one site followed by validation on the other site. Using the new model with parameters estimated on Krycklan data, biomass in Remningstorp is retrieved with RMSE of 40-59 tons/ha, or 22-33% of the mean biomass, which is lower compared to the other models. In the inverse scenario, the examined site is not well represented in the training data set, and the results are therefore not conclusive

Terrestrial laser scanning for plot-scale forest measurement

Plot-scale measurements have been the foundation for forest surveys and reporting for over 200 years. Through recent integration with airborne and satellite remote sensing, manual measurements of vegetation structure at the plot scale are now the basis for landscape, continental and international mapping of our forest resources. The use of terrestrial laser scanning (TLS) for plot-scale measurement was first demonstrated over a decade ago, with the intimation that these instruments could replace manual measurement methods. This has not yet been the case, despite the unparalleled structural information that TLS can capture. For TLS to reach its full potential, these instruments cannot be viewed as a logical progression of existing plot-based measurement. TLS must be viewed as a disruptive technology that requires a rethink of vegetation surveys and their application across a wide range of disciplines. We review the development of TLS as a plotscale measurement tool, including the evolution of both instrument hardware and key data processing methodologies. We highlight two broad data modelling approaches of gap probability and geometrical modelling and the basic theory that underpins these. Finally, we discuss the future prospects for increasing the utilisation of TLS for plot-scale forest assessment and forest monitoring

Terrestrial laser scanning for vegetation analyses with a special focus on savannas

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome