Extracting More Data from LiDAR in Forested Areas by Analyzing Waveform Shape

Light Detection And Ranging (LiDAR) in forested areas is used for constructing Digital Terrain Models (DTMs), estimating biomass carbon and timber volume and estimating foliage distribution as an indicator of tree growth and health. All of these purposes are hindered by the inability to distinguish the source of returns as foliage, stems, understorey and the ground except by their relative positions. The ability to separate these returns would improve all analyses significantly. Furthermore, waveform metrics providing information on foliage density could improve forest health and growth estimates. In this study, the potential to use waveform LiDAR was investigated. Aerial waveform LiDAR data were acquired for a New Zealand radiata pine plantation forest, and Leaf Area Density (LAD) was measured in the field. Waveform peaks with a good signal-to-noise ratio were analyzed and each described with a Gaussian peak height, half-height width, and an exponential decay constant. All parameters varied substantially across all surface types, ruling out the potential to determine source characteristics for individual returns, particularly those with a lower signal-to-noise ratio. However, pulses on the ground on average had a greater intensity, decay constant and a narrower peak than returns from coniferous foliage. When spatially averaged, canopy foliage density (measured as LAD) varied significantly, and was found to be most highly correlated with the volume-average exponential decay rate. A simple model based on the Beer-Lambert law is proposed to explain this relationship, and proposes waveform decay rates as a new metric that is less affected by shadowing than intensity-based metrics. This correlation began to fail when peaks with poorer curve fits were included

Lidar Remote Sensing Of Forest Canopy Structure: An Assessment Of The Accuracy Of Lidar And Its Relationship To Higher Trophic Levels

Light detection and ranging (LiDAR) data can provide detailed information about three-dimensional forest horizontal and vertical structure that is important to forest productivity and wildlife habitat. Indeed, LiDAR data have been shown to provide accurate estimates to forest structural parameters and measures of higher trophic levels (e.g., avian abundance and diversity). However, links between forest structure and tree function have not been evaluated using LiDAR. This study was designed and scaled to assess the relationship of LiDAR to multiple aspects of forest structure and higher trophic levels (arthropod and bird populations), which included the ground-based collection of percent crown and understory closure, as well as arthropod and avian abundance and diversity data. Additional plot-based measures were added to assess the relationship of LiDAR to forest health and productivity. High-resolution discrete-return LiDAR data (flown summer of 2009) were acquired for the Hubbard Brook Experimental Forest (HBEF) in New Hampshire, USA. LiDAR data were classified into four canopy structural categories: 1) high crown and high understory closure, 2) high crown and low understory closure, 3) low crown and high understory closure, and 4) low crown and low understory closure. Nearby plots from each of the four LiDAR categories were grouped into blocks to assess the spatial consistency of data. Ground-based measures of forest canopy structure, site, stand and individual tree measures were collected on nine 50 m-plots from each LiDAR category (36 plots total), during summer of 2012. Analysis of variance was used to assess the relationships between LiDAR and a suite of tree function measures. Our results show the novel ability of LiDAR to assess forest health and productivity at the stand and individual tree level. We found significant correspondence between LiDAR categories and our ground-based measures of tree function, including xylem increment growth, foliar nutrition, crown health, and stand mortality. Furthermore, we found consistent reductions in xylem increment growth, decreases in foliar nutrition and crown health, and increases in stand mortality related to high understory closure. This suggests that LiDAR measures can reflect competitive interactions, not just among overstory trees for light, but also interactions between overstory trees and understory vegetation for resources other than light (e.g., nutrients). High-resolution LiDAR data show promise in the assessment of forest health and productivity related to tree function

Using Sentinel-2 and canopy height models to derive a landscape-level biomass map covering multiple vegetation types

Vegetation biomass is a globally important climate-relevant terrestrial carbon pool and also drives local hydrological systems via evapotranspiration. Vegetation biomass of individual vegetation types has been successfully estimated from active and passive remote sensing data. However, for many tasks, landscape-level biomass maps across several vegetation types are more suitable than biomass maps of individual vegetation types. For example, the validation of ecohydrological models and carbon budgeting typically requires spatially continuous biomass estimates, independent from vegetation type. Studies that derive biomass estimates across multiple vegetation or land-cover types to merge them into a single landscape-level biomass map are still scarce, and corresponding workflows must be developed. Here, we present a workflow to derive biomass estimates on landscape-level for a large watershed in central Chile. Our workflow has three steps: First, we combine field plotbased biomass estimates with spectral and structural information collected from Sentinel-2, TanDEM-X and airborne LiDAR data to map grassland, shrubland, native forests and pine plantation biomass using random forest regressions with an automatic feature selection. Second, we predict all models to the entire landscape. Third, we derive a land-cover map including the four considered vegetation types. We then use this land-cover map to assign the correct vegetation type-specific biomass estimate to each pixel according to one of the four considered vegetation types. Using a single repeatable workflow, we obtained biomass predictions comparable to earlier studies focusing on only one of the four vegetation types (Spearman correlation between 0.80 and 0.84; normalized-RMSE below 16 % for all vegetation types). For all woody vegetation types, height metrics were amongst the selected predictors, while for grasslands, only Sentinel-2 bands were selected. The land-cover was also mapped with high accuracy (OA = 83.1 %). The final landscape-level biomass map spatially agrees well with the known biomass distribution patterns in the watershed. Progressing from vegetation-type specific maps towards landscape-level biomass maps is an essential step towards integrating remote-sensing based biomass estimates into models for water and carbon management

Capability of GLAS/ICESat data to estimate forest canopy height and volume in mountainous forests of Iran

International audienceThe importance of measuring biophysical properties of forest for ecosystem health monitoring and forest management encourages researchers to find precise, yet low cost methods especially in mountainous and large area. In the present study Geoscience Laser Altimeter System (GLAS) on board ICESat was used to estimate three biophysical characteristics of forests located in north of Iran: 1) maximum canopy height (Hmax), 2) Lorey's height (HLorey), and 3) Forest volume (V). A large number of Multiple Linear Regressions (MLR) and also Random Forest (RF) regressions were developed using different set of variables: waveform metrics, Principal Components (PCs) produced from Principal Component Analysis (PCA) and Wavelet Coefficients (WCs) generated from wavelet transformation. To validate and compare different models, statistical criteria were calculated based on a five-fold cross validation. The best model concerning the maximum canopy height was an MLR with an RMSE of 5.0 m which combined two metrics extracted from waveforms (waveform extent "Wext" and height at 50% of waveform energy "H50"), and one from the Digital Elevation Model (Terrain Index: TI). The mean absolute error (MAPE) of maximum canopy height estimates is about 16.4%. For Lorey's height, a simple MLR model including two metrics (Wext and TI) represents the highest performance (RMSE=5.1 m, MAPE=24.0%). Totally, MLR models showed better performance rather than RF models, and accuracy of height estimations using waveform metrics was greater than those based on PCs or WCs. Concerning forest volume, employing regression models to estimate volume directly from GLAS data led to a better result (RMSE=128.8 m3/ha) rather than volume-HLorey relationship (RMSE=167.8 m3/ha)

Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

Peer reviewe

Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon

Abstract Background Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results Power-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind. </jats:sec

Estimation of aerial biomass using discrete-wave LiDAR data in combination with different vegetation indices in plantations of Pinus radiata (D. DON), Región del Maule, Chile.

The aerial biomass of Pinus radiata plantations in the Región del Maule, Chile, was estimated from linear models using databases of LiDAR and multispectral LANDSAT ETM+. Six descriptive height variables were obtained from the LiDAR point cloud; the 25%, 50%, 75%, 95% and 100% percentiles and the mean height. Two variables associated with the density of points were also obtained, which relate the returns between fixed weighted intervals calculated as a function of the observed biomass. For multispectral variables we used NDVI, corrected NVDI (NDVIc) and the “Tasseled Cap” components brilliance, greenness and humidity. The results showed coefficients of determination (R2) between 0.801 and 0.814, with errors between 36.07 and 36.11 ton ha-1 for the models generated using height percentiles, and R2 from 0.807 to 0.823 with errors between 36.06 and 36.84 ton ha-1 for transformed LiDAR data. Finally, the stepwise model using all available variables had R2 of 0.821-0.835 with errors of 34.28 - 36.31 ton ha-1.La biomasa aérea en bosques de pino insigne en la región del Maule, Chile, fue estimada utilizando modelos lineales sobre la base de datos LiDAR y multiespectrales de LANDSAT ETM+. De la nube de puntos LiDAR se obtuvo un total de seis variables descriptivas de altura, los percentiles 25% , 50%, 75% , 95% , 100% y la altura promedio, y dos variables asociadas a la densidad de puntos, las cuales relacionan los retornos entre intervalos fijos ponderadores calculados en función de la biomasa observada. Para las variables multiespectrales, se utilizó: El NDVI, el NDVI corregido (NDVIc) y los componentes “Tasseled Cap” Brillantez, Verdor y Humedad. Los resultados mostraron coeficientes de determinación (R2) entre 0,801 y 0,814 con errores entre 36,07 y 36,11 ton ha-1 para los modelos generados a partir de percentiles de altura y R2 entre 0,807 y 0,823 con errores entre 36,06 y 36,84 ton ha-1 para datos de transformaciones de información LiDAR. Finalmente, el modelo “Stepwise” que involucra todas las variables disponibles tiene un ajuste de R2 entre 0,821 y 0,835 con errores entre 34,28 y 36,31 ton ha-1

The impact of logging on vertical canopy structure across a gradient of tropical forest degradation intensity in Borneo

Forest degradation through logging is pervasive throughout the world's tropical forests, leading to changes in the three-dimensional canopy structure that have profound consequences for wildlife, microclimate and ecosystem functioning. Quantifying these structural changes is fundamental to understanding the impact of degradation, but is challenging in dense, structurally complex forest canopies. We exploited discrete-return airborne LiDAR surveys across a gradient of logging intensity in Sabah, Malaysian Borneo, and assessed how selective logging had affected canopy structure (Plant Area Index, PAI, and its vertical distribution within the canopy). LiDAR products compared well to independent, analogue models of canopy structure produced from detailed ground-based inventories undertaken in forest plots, demonstrating the potential for airborne LiDAR to quantify the structural impacts of forest degradation at landscape scale, even in some of the world's tallest and most structurally complex tropical forests. Plant Area Index estimates across the plot network exhibited a strong linear relationship with stem basal area (R2 = 0.95). After at least 11–14 years of recovery, PAI was ~28% lower in moderately logged plots and ~52% lower in heavily logged plots than that in old-growth forest plots. These reductions in PAI were associated with near-complete lack of trees >30-m tall, which had not been fully compensated for by increasing plant area lower in the canopy. This structural change drives a marked reduction in the diversity of canopy environments, with the deep, dark understorey conditions characteristic of old-growth forests far less prevalent in logged sites. Full canopy recovery is likely to take decades. Synthesis and applications. Effective management and restoration of tropical forests requires detailed monitoring of the forest and its environment. We demonstrate that airborne LiDAR can effectively map the canopy architecture of the complex tropical forests of Borneo, capturing the three-dimensional impact of degradation on canopy structure at landscape scales, therefore facilitating efforts to restore and conserve these ecosystems

Individual tree detection and modelling aboveground biomass and forest parameters using discrete return airborne LiDAR data

Individual tree detection and modelling forest parameters using Airborne Laser Scanner data (Light Detection and Ranging (LiDAR) is becoming increasingly important for the monitoring and sustainable management of forests. Remote sensing has been a useful tool for individual tree analysis in the past decade, although inadequate spatial resolution from satellites means that only airborne systems have sufficient spatial resolution to conduct individual tree analysis. Moreover, recent advances in airborne LiDAR now provide high horizontal resolution as well as information in the vertical dimension. However, it is challenging to fully exploit and utilize small-footprint LiDAR data for detailed tree analysis. Procedures for forest biomass quantification and forest attributes measurement using LiDAR data have improved at a rapid pace as more robust and sophisticated modelling used to improve the studies. This thesis contains an evaluation of three approaches of utilizing LiDAR data for individual tree forest measurement. The first explores the relationship between LiDAR metrics and field reference to assess the correlation between LiDAR and field data at the individual-tree level. The intention was not to detect trees automatically, but to develop a LiDAR-AGB model based on trees that were mapped in the field so as to evaluate the relationships between LiDAR-type metrics under controlled conditions for the study sites, and field-derived AGB. A non-linear AGB model based on field data and LiDAR data was developed and LiDAR height percentile h80 and crown width measurement (CW) was found to best fit the data as evidenced by and Adj-R2 value of 0.63, the root mean squared error of the model of 14.8% and analysis of the residuals. This paper provides the foundation for a predictive LiDAR-AGB model at tree level over two study sites, Pasoh Forest Reserve and FRIM Forest Reserve. The second part of the thesis then takes this AGB-LiDAR relationship and combines it with individual tree crown delineation. This chapter shows the contribution of performing an automatic individual tree crown delineation over the wider forest areas. The individual tree crown delineation is composed of a five-step framework, which is unique in its automated determination of dominant crown sizes in a forest area and its adaption of the LiDAR-AGB model developed for the purpose of validation the method. This framework correctly delineated 84% and 88% of the tree crowns in the two forest study areas which is mostly dominated with lowland dipterocarp trees. Thirdly, parametric and non-parametric modelling approaches are proposed for modelling forest structural attributes. Selected modelling methods are compared for predicting 4 forest attributes, volume (V), basal area (BA), height (Ht) and aboveground biomass (AGB) at the species level. The AGB modelling in this paper is extracted using the LiDAR derived variables from the automated individual tree crown delineation, in contrast to the earlier AGB modelling where it is derived based on the trees that were mapped in the field. The selected non-parametric method included, k-nearest neighbour (k-NN) imputation methods: Most Similar Neighbour (MSN) and Gradient Nearest Neighbour (GNN), Random Forest (RF) and parametric approach: Ordinary Least Square (OLS) regression. To compare and evaluate these approaches a scaled root mean squared error (RMSE) between observed and predicted forest attribute sampled from both forest site was computed. The best method varied according to response variable and performance measure. OLS regression was to found to be the best performance method overall evidenced by RMSE after cross validation for BA (1.40 m2), V (1.03 m3), Ht (2.22 m) and AGB (96 Kg/tree) respectively, showed its applicability to wider conditions, while RF produced best overall results among the non-parametric methods tested. This thesis concludes with a discussion of the potential of LiDAR data as an independent source of important forest inventory data source when combined with appropriate designed sample plots in the field, and with appropriate modelling tools