Felix Morsdorf

An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning

The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppa (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test. Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future.

Forest Canopy Gap Fraction From Terrestrial Laser Scanning

A terrestrial laser scanner (TLS) was used to measure canopy directional gap fraction distribution in forest stands in the Swiss National Park, eastern Switzerland. A scanner model was derived to determine the expected number of laser shots in all directions, and these data were compared with the measured number of laser hits to determine directional gap fraction at eight sampling points. Directional gap fraction distributions were determined from digital hemispherical photographs recorded at the same sampling locations in the forest, and these data were compared with distributions computed from the laser scanner data. The results showed that the measured directional gap fraction distributions were similar for both hemispherical photography and TLS data with a high degree of precision in the area of overlap of orthogonal laser scans. Analysis of hemispherical photography to determine canopy gap fraction normally requires some manual data processing; laser scanners offer semiautomatic measurement of directional gap fraction distribution plus additional three-dimensional information about tree height, gap size, and foliage distributions

Inversion of a lidar waveform model for forest biophysical parameter estimation

Due to its measurement principle, light detection and ranging (lidar) is particularly suited to estimate the horizontal as well as vertical distribution of forest structure. Quantification and characterization of forest structure is important for the understanding of the forest ecosystem functioning and, moreover, will help to assess carbon sequestration within forests. The relationship between the signal recorded by a lidar system and the canopy structure of a forest can be accurately characterized by physically based radiative transfer models (RTMs). A three-dimensional RTM is capable of representing the complex forest canopy structure as well as the involved physical processes of the lidar pulse interactions with the vegetation. Consequently, the inversion of such an RTM presents a novel concept to retrieve biophysical forest parameters that exploits the full lidar signal and underlying physical processes. A synthetic dataset and data acquired in the Swiss National Park (SNP) successfully demonstrated the feasibility and the potential of RTM inversion to retrieve forest structure from large-footprint lidar waveform data. The SNP lidar data consist of waveforms generated from the aggregation of small-footprint lidar returns. Derived forest biophysical parameters, such as fractional cover, leaf area index, maximum tree height, and the vertical crown extension, were able to describe the horizontal and vertical forest canopy structure.

A Multispectral Canopy LiDAR Demonstrator Project

The first demonstration of a multispectral light detection and ranging (LiDAR) optimized for detailed structure and physiology measurements in forest ecosystems is described. The basic principle is to utilize, in a single instrument, both the capacity of multispectral sensing to measure plant physiology [through normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI)] with the ability of LiDAR to measure vertical structure information and generate “hot spot” (specular) reflectance data independent of solar illumination. A tunable laser operated at four wavelengths (531, 550, 660, and 780 nm) was used to measure profiles of the NDVI and the PRI. Laboratory-based measurements were conducted for live trees, demonstrating that realistic values of the indexes can be measured. A model-based analysis demonstrates that the LiDAR waveforms cannot only capture the tree height information but also picks up the seasonal and vertical variation of NDVI inside the tree canopy.

Status and prospects for LiDAR remote sensing of forested ecosystems

The science associated with the use of airborne and satellite Light Detection and Ranging (LiDAR) to remotely sense forest structure has rapidly progressed over the past decade. LiDAR has evolved from being a poorly understood, potentially useful tool to an operational technology in a little over a decade, and these instruments have become a major success story in terms of their application to the measurement, mapping, or monitoring of forests worldwide. Invented in 1960, the laser and, a short time later, LiDAR, were found in research and military laboratories. Since the early 2000s, commercial technological developments coupled with an improved understanding of how to manipulate and analyze large amounts of collected data enabled notable scientific and application developments. A diversity of rapidly developing fields especially benefit from communications offered through conferences such as SilviLaser, and LiDAR has been no different. In 2002 the SilviLaser conference series was initiated to bring together those interested in the development and application of LiDAR for forested environments. Now, a little over a decade later, commercial use of LiDAR is common. In this paper – using the deliberations of SilviLaser 2012 as a source of information – we aim to capture aspects of importance to LiDAR users in the forest ecosystems community and to also point to key emerging issues as well as some remaining challenges.

Assessment of the influence of flying altitude and scan angle on biophysical vegetation products derived from airborne laser scanning

Airborne Laser Scanning (ALS) has been established as a valuable tool for the estimation of biophysical vegetation properties such as tree height, crown width, fractional cover and leaf area index (LAI). It is expected that the conditions of data acquisition, such as viewing geometry and sensor configuration influence the value of these parameters. In order to gain knowledge about these different conditions, we test for the sensitivity of vegetation products for viewing geometry, namely flying altitude and scanning (incidence) angle. Based on two methodologies for single tree extraction and derivation of fractional cover and LAI previously developed and published by our group, we evaluate how these variables change with either flying altitude or scanning angle. These are the two parameters which often need to be optimized towards the best compromise between point density and area covered with a single flight line, in order to reduce acquisition costs. Our test‐site in the Swiss National Park was sampled with two nominal flying altitudes, 500 and 900 m above ground. Incidence angle and local incidence angle were computed based on the digital terrain model using a simple backward geocoding procedure. We divided the raw laser returns into several different incident angle classes based on the flight path data; the TopoSys Falcon II system used in this study has a maximum scan angle of ±7.15°. We compared the derived biophysical properties from each of these classes with field measurements based on tachymeter measurements and hemispherical photographs, which were geolocated using differential GPS. It was found that with increasing flying height the well‐known underestimation of tree height increases. A similar behaviour can be observed for fractional cover; its respective values decrease with higher flying height. The minimum distance between first and last echo increases from 1.2 metres for 500 m AGL to more than 3 metres for 900 m AGL, which does alter return statistics. The behaviour for incidence angles is not so evident, probably due to the small scanning angle of the system used. fCover seems to be most affected by incidence angles, with significantly higher differences for locations further away from nadir. As expected, incidence angle appears to be of higher importance for vegetation density parameters than local incidence angle.

Tomographic Imaging of a Forested Area By Airborne Multi-Baseline P-Band SAR

In recent years, various attempts have been undertaken to obtain information about the structure of forested areas from multi-baseline synthetic aperture radar data. Tomographic processing of such data has been demonstrated for airborne L-band data but the quality of the focused tomographic images is limited by several factors. In particular, the common Fourier-based focusing methods are susceptible to irregular and sparse sampling, two problems, that are unavoidable in case of multi-pass, multi-baseline SAR data acquired by an airborne system. In this paper, a tomographic focusing method based on the time-domain back-projection algorithm is proposed, which maintains the geometric relationship between the original sensor positions and the imaged target and is therefore able to cope with irregular sampling without introducing any approximations with respect to the geometry. The tomographic focusing quality is assessed by analysing the impulse response of simulated point targets and an in-scene corner reflector. And, in particular, several tomographic slices of a volume representing a forested area are given. The respective P-band tomographic data set consisting of eleven flight tracks has been acquired by the airborne E-SAR sensor of the German Aerospace Center (DLR).

International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning

Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data.

Mapping functional diversity from remotely sensed morphological and physiological forest traits

Assessing functional diversity from space can help predict productivity and stability of forest ecosystems at global scale using biodiversity–ecosystem functioning relationships. We present a new spatially continuous method to map regional patterns of tree functional diversity using combined laser scanning and imaging spectroscopy. The method does not require prior taxonomic information and integrates variation in plant functional traits between and within plant species. We compare our method with leaf-level field measurements and species-level plot inventory data and find reasonable agreement. Morphological and physiological diversity show consistent change with topography and soil, with low functional richness at a mountain ridge under specific environmental conditions. Overall, functional richness follows a logarithmic increase with area, whereas divergence and evenness are scale invariant. By mapping diversity at scales of individual trees to whole communities we demonstrate the potential of assessing functional diversity from space, providing a pathway only limited by technological advances and not by methodology.As remote sensing technology improves, it is now possible to map fine-scale variation in plant functional traits. Schneider et al. remotely sense tree functional diversity, validate with field data, and reveal patterns of plant adaptation to the environment previously not retrievable from plot data

Tomographic processing of multi-baseline P-band SAR data for imaging of a forested area

Recently, various attempts have been undertaken to obtain information about the structure of forested areas from multi-baseline synthetic aperture radar data. Tomographic processing of such data has been demonstrated but the quality of the focused tomographic image is limited by several factors. In particular Fourier-based focusing methods are susceptible to irregular and sparse sampling, two problems, that are unavoidable in case of multi-pass, multi-baseline SAR data acquired by an airborne system. We propose a tomographic focusing method based on the time-domain back-projection algorithm, which maintains the geometric relationship between the original sensor positions and the imaged target and is therefore able to cope with irregular sampling without introducing any approximations with respect to the geometry. We assess the tomographic focusing quality with the help of the impulse response of simulated point targets and an in-scene corner reflector. And, in particular, preliminary results obtained with the newly acquired P-band tomographic data set consisting of eleven flight tracks are presented.