Small-footprint Airborne LiDAR Research Articles

Shrub characterization using terrestrial laser scanning and implications for airborne LiDAR assessment

Sagebrush-steppe ecosystems across the United States Intermountain West are experiencing major structural and functional changes. Scientists and managers need effective technologies to understand such dynamic changes across broad spatial scales. We tested the capacity of terrestrial laser scanning (TLS) to automatically determine structural information of individual shrubs (principally Artemisia tridentata) and shrub canopies in eastern Washington, USA. Because current airborne LiDAR systems have technological constraints that may limit their utility in shrub-dominated ecosystems, we used the TLS data both in their basic form and to simulate high resolution discrete-return airborne LiDAR data with sample densities of 4 and 16 points m−2. Through spatial wavelet analysis we automatically detected the locations of up to 78% of all individual shrubs identified in the field and up to 88% of shrubs with a crown diameter > 1.5 m. Shrub height and canopy cover derived from TLS data were significantly correlated with field measurements (respectively, r2 = 0.94 and 0.51, p < 0.001, α = 0.05). Automated detection of individual shrub crown area using the high resolution simulated airborne LiDAR dataset was also significantly correlated with field measurements (r2 = 0.47, p ≤ 0.01). Our results indicate that TLS data are useful for automatic quantification of shrub structure and provide a glimpse to the utility of the next generation of small-footprint airborne LiDAR instruments in quantifying shrub biophysical parameters across broad areas.

Range determination for generating point clouds from airborne small footprint LiDAR waveforms

This paper presents a range determination approach for generating point clouds from small footprint LiDAR waveforms. Waveform deformation over complex terrain area is simulated using convolution. Drift of the peak center position is analyzed to identify the first echo returned by the illuminated objects in the LiDAR footprint. An approximate start point of peak in the waveform is estimated and adopted as the indicator of range calculation; range correction method is proposed to correct pulse widening over complex terrain surface. The experiment was carried out on small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. The results suggest that the proposed approach generates more points than standard commercial products; based on field measurements, a comparative analysis between the point clouds generated by the proposed approach and the commercial software GeocodeWF indicates that: 1). the proposed approach obtained more accurate tree heights; 2). smooth surface can be achieved with low standard deviation. In summary, the proposed approach provides a satisfactory solution for range determination in estimating 3D coordinate values of point clouds, especially for correcting range information of waveforms containing deformed peaks.

A fine-scale architectural model of trees to enhance LiDAR-derived measurements of forest canopy structure

Forest canopy structure consists of the complex spatial arrangement of the foliage, branches, and boles of trees. Measuring forest canopy structure is an active research question because of its influence on a wide range of biophysical and ecological processes. Light detection and ranging technology (LiDAR), both with airborne and terrestrial instruments, has been used to assess canopy structure and estimate vegetation attributes. Although LiDAR has proven to be an effective tool for mapping the forest canopy, its ability to probe all canopy elements is limited by the signal occlusion that occurs when multiple objects lie between the sensor and target area. This limitation can be alleviated by comparing realistic fine-scale 3D canopy architectural models with terrestrial and airborne LiDAR point-cloud data sets. We propose a method that mitigates the limitations of object occlusion and allows detailed description of 3D canopy structure from small-footprint airborne LiDAR (ALiDAR). Our approach involved reconstructing stem-mapped forest stands with catalogs of terrestrial LiDAR (TLiDAR) scans of individual trees, using an architectural model called L-Architect (LiDAR data to vegetation Architecture). L-Architect has been developed to link TLiDAR scans to tree-level structural attributes that reproduce the branching structure and foliage spatial distribution of a tree according to information gathered from detailed mensurational data. This study has further developed the potential of L-Architect to reconstruct individual trees to model the structure of a stand. The method was evaluated by comparing vertical profiles of material density for a forest stand calculated from TLiDAR scans and L-Architect's canopy model, resulting in a Pearson's correlation coefficient of 0.95. Most individual tree structures were reproduced with high accuracy, with a relative deviation of <10% on tree diameter at breast height (DBH) and total leaf area. The use of the ALiDAR data together with L-Architect allowed quantifying how significant object occlusion affects untreated data. We obtained mean correlations between ALiDAR data sets and L-Architect derived profiles of 0.84 and 0.43 for the vertical and horizontal material distribution, respectively. We demonstrate how L-Architect can be used to make accurate 3D maps of stand components from LiDAR data.

Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure

We predict stand basal area (BA) from small footprint LiDAR data in 129 one-ha tropical forest plots across four sites in French Guiana and encompassing a great diversity of forest structures resulting from natural (soil and geological substrate) and anthropogenic effects (unlogged and logged forests). We use predictors extracted from the Canopy Height Model to compare models of varying complexity: single or multiple regressions and nested models that predict BA by independent estimates of stem density and quadratic mean diameter. Direct multiple regression was the most accurate, giving a 9.6% Root Mean Squared Error of Prediction (RMSEP). The magnitude of the various errors introduced during the data collection stage is evaluated and their contribution to MSEP is analyzed. It was found that these errors accounted for less than 10% of model MSEP, suggesting that there is considerable scope for model improvement. Although site-specific models showed lower MSEP than global models, stratification by site may not be the optimal solution. The key to future improvement would appear to lie in a stratification that captures variations in relations between LiDAR and forest structure.

Inversion of leaf area index based on small-footprint waveform airborne LIDAR

In order to study the detection of vertical structure information of vegetation, a simulation of LIDAR return waveform based on the single scattering of crown reflectance model and the Gaussian character of incidence and return waveform was proposed. The corresponding inversed method was established to calculate the crop leaf area density and leaf area index. The sensitivity analysis illustrated G function had a larger impact on inversed results than soil and leaf reflectivity. Finally, the data of Water (Watershed Airborne Telemetry Experimental Research) was used to validate this method. The results showed that the inversed leaf area density was consistent with the field measurement data, and the relative error of the inversed LAI was 12.5%. Therefore, this method can provide a new way to detect the vegetation structural parameters.

Bidirectional texture function of high resolution optical images of tropical forest: An approach using LiDAR hillshade simulations

Quantifying and monitoring the structure and degradation of tropical forests over regional to global scales is gaining increasing scientific and societal importance. Reliable automated methods are only beginning to appear; for instance, through the recent development of textural approaches applied to high resolution optical imagery. In particular, the Fourier Transform Textural Ordination (FOTO) method shows some potential to provide non-saturating estimates of tropical forest structure, including for large scale applications. However, we need to understand more precisely how canopy structure interacts with physical signals (light) to produce a given texture, notably to assess the method's sensitivity to varying sun-view acquisition conditions. In this study, we take advantage of the detailed description of canopy topography provided by airborne small footprint LiDAR data acquired over the Paracou forest experimental station in French Guiana. Using hillshade models and a range of sun-view angles identical to the actual parameter distributions found for Quickbird™ images over the Amazon, we study noise and bias in texture estimation induced by the changing configurations. We introduce the bidirectional texture function, which summarizes these effects, and in particular the existence of a textural ‘hot spot’, similar to a well-known feature of bidirectional reflectance studies. For texture, this effect implies that coarseness decreases in configurations for which shadows are concealed to the observer. We also propose a method, termed partitioned standardization, that allows mitigating acquisition effects and discuss the potential for an operational use of VHR optical imagery and the FOTO method in the current context of international decisions to reduce CO 2 emissions due to deforestation and forest degradation.

Comparison of Different Sampling Density Data for Detecting and Measuring Individual-trees in a Mountainous Coniferous Forest using Small-footprint Airborne LiDAR(&lt;Special Issue&gt;Silvilaser)

Comparison of Different Sampling Density Data for Detecting and Measuring Individual-trees in a Mountainous Coniferous Forest using Small-footprint Airborne LiDAR(&lt;Special Issue&gt;Silvilaser)

Quantifying forest above ground carbon content using LiDAR remote sensing

The UNFCCC and interest in the source of the missing terrestrial carbon sink are prompting research and development into methods for carbon accounting in forest ecosystems. Here we present a canopy height quantile-based approach for quantifying above ground carbon content (AGCC) in a temperate deciduous woodland, by means of a discrete-return, small-footprint airborne LiDAR. Fieldwork was conducted in Monks Wood National Nature Reserve UK to estimate the AGCC of five stands from forest mensuration and allometric relations. In parallel, a digital canopy height model (DCHM) and a digital terrain model (DTM) were derived from elevation measurements obtained by means of an Optech Airborne Laser Terrain Mapper 1210. A quantile-based approach was adopted to select a representative statistic of height distributions per plot. A forestry yield model was selected as a basis to estimate stemwood volume per plot from these heights metrics. Agreement of r=0.74 at the plot level was achieved between ground-based AGCC estimates and those derived from the DCHM. Using a 20×20 m grids superposed to the DCHM, the AGCC was estimated at the stand level and at the woodland level. At the stand level, the agreement between the plot data upscaled in proportion to area and the LiDAR estimates was r=0.85. At the woodland level, LiDAR estimates were nearly 24% lower than those from the upscaled plot data. This suggests that field-based approaches alone may not be adequate for carbon accounting in heterogeneous forests. Conversely, the LiDAR 20×20 m grid approach has an enhanced capability of monitoring the natural variability of AGCC across the woodland.