On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements

The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWEL's novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultaneous scanning at both 1064 nm and 1548 nm by DWEL instruments provides a new spectral dimension to TLS data that joins the 3D spatial dimension of lidar as an information source. Our point cloud classification algorithm explores the utilization of both spectral and spatial attributes of individual points from DWEL scans and highlights the strengths and weaknesses of each attribute domain. The spectral and spatial attributes for vegetation element classification each perform better in different parts of vegetation (canopy interior, fine branches, coarse trunks, etc.) and under different vegetation conditions (dead or live, leaf-on or leaf-off, water content, etc.). These environmental characteristics of vegetation, convolved with the lidar instrument specifications and lidar data quality, result in the actual capabilities of spectral and spatial attributes to classify vegetation elements in 3D space. The spectral and spatial information domains thus complement each other in the classification process. The joint use of both not only enhances the classification accuracy but also reduces its variance across the multiple vegetation types we have examined, highlighting the value of the DWEL as a new source of 3D spectral information. Wider deployment of the DWEL instruments is in practice currently held back by challenges in instrument development and the demands of data processing required by coaxial dual- or multi-wavelength scanning. But the simultaneous 3D acquisition of both spectral and spatial features, offered by new multispectral scanning instruments such as the DWEL, opens doors to study biophysical and biochemical properties of forested and agricultural ecosystems at more detailed scales.

The terrestrial laser scanning revolution in forest ecology

New laser scanning technologies are set to revolutionize the way in which we measure and understand changes in ecosystem structure and function. Forest ecosystems present a particular challenge because of their scale, complexity and structural dynamics. Traditional forestry techniques rely on manual measurement of easy-to-measure characteristics such as tree girth and height, along with time-consuming, logistically difficult and error-prone destructive sampling. Much more detailed and accurate three-dimensional measurements of forest structure and composition are key to reducing errors in biomass estimates and carbon dynamics and to better understanding the role of forests in global ecosystem and climate change processes. Terrestrial laser scanners are now starting to be deployed in forest ecology research and, at the same time, new terrestrial laser scanning (TLS) technologies are being developed to enhance and extend the range of measurements that can be made. These new TLS measurements provide a tantalizing glimpse of a completely new way to measure and understand forest structure. It is therefore a good time to take stock, assess the state of the art and identify the immediate challenges for continued development of TLS in forest ecology.

Classifying ecosystems with metaproperties from terrestrial laser scanner data

In this study, we introduce metaproperty analysis of terrestrial laser scanner (TLS) data, and demonstrate its application through several ecological classification problems. Metaproperty analysis considers pulse level and spatial metrics derived from the hundreds of thousands to millions of lidar pulses present in a single scan from a typical contemporary instrument. In such large aggregations, properties of the populations of lidar data reflect attributes of the underlying ecological conditions of the ecosystems.In this study, we provide the Metaproperty Classification Model to employ TLS metaproperty analysis for classification problems in ecology. We applied this to a proof-of-concept study, which classified 88 scans from rooms and forests with 100% accuracy, to serve as a template.We then applied the Metaproperty Classification Model in earnest, to separate scans from temperate and tropical forests with 97.09% accuracy (N = 224), and to classify scans from inland and coastal tropical rainforests with 84.07% accuracy (N = 270).The results demonstrate the potential for metaproperty analysis to identify subtle and important ecosystem conditions, including diseases and anthropogenic disturbances. Metaproperty analysis serves as an augmentation to contemporary object reconstruction applications of TLS in ecology, and can characterize regional heterogeneity.