Characterization of forest canopy structure using ...

Remote Sensing Series

68

Reik Leiterer

Characterization of forest canopy structure using airborne laser scanning

Remote Sensing Laboratories Department of Geography University of Zurich, 2016

Promotion Committee Prof. Dr. Michael E. Schaepman Prof. Dr. Reinhard Furrer Prof. Dr. Harald Bugmann Dr. Felix Morsdorf

Dissertation

Characterization of forest canopy structure using airborne laser scanning Author: Reik Leiterer Remote Sensing Laboratories Department of Geography/ Faculty of science University of Zurich Winterthurerstrasse 190 CH-8057 Zurich Switzerland http://www.geo.uzh.ch/en/units/rsl

April, 2016 – All rights reserved.

Summary

Forests cover approximately one third of Earth’s total land area and play a pivotal role for global biogeochemical and biophysical cycles, contribute to maintaining the terrestrial biodiversity and provide a wide range of valuable ecosystem goods and services, including food, timber and climate moderation. The structure of forest canopies is considered a particularly crucial constituent of forest ecosystem functioning and processes, as it serves several purposes, e.g., influences the energy fluxes between the atmosphere and the forest, serves as an indicator for forest stand resistance to disturbances, enables the estimation of the conservation potential for biodiversity and allows the identification of recruitment limitations. Forest canopy structure itself is not a measurable quantity, but individual properties of the canopy structure can be described by means of a wide variety of canopy structure variables. Airborne laser scanning (ALS), a recent emerging Earth observation technology, has greatly improved the ability to derive canopy structure variables over large areas. In this thesis, we investigated the feasibility and the benefit of using ALS for an automatic, comprehensive and accurate characterization of canopy structure in mixed, temperate forests. We improved and developed methods for an accurate derivation of a series of canopy structure variables, suitable for applications in forest inventory and management, forest ecology and radiative transfer modelling. In this context, we analysed the influence of ALS data properties in combination with an estimation of the influence of spatial scales on canopy structure characterization. We developed the fully automatic and data-driven concept of canopy structure types (CSTs), which works towards bridging the gap between the usually used physically oriented approaches on individual tree level and the area-based empirical regression methods: following objective criteria, but less scale-dependent and with consideration of the specific ALS data properties. We discuss advantages and limitations of the presented methods and concepts in addition to suggesting possible improvements and future perspectives. In this context, we pay particular attention to the challenge of establishing a new concept of forest canopy structure characterization with respect to presented and future fields of application.

Zusammenfassung

Wälder bedecken ca. ein Drittel der Landoberfläche der Erde. Sie spielen eine zentrale Rolle in den globalen biogeochemischen und biophysikalischen Kreisläufen, tragen zum Erhalt der terrestrischen Biodiversität bei und stellen eine Vielzahl an Ökosystemgütern und –dienstleistungen bereit. Ein besonders wichtiger Bestandteil von Waldökosystemen ist die Waldstruktur. Sie beeinflusst die Energieflüsse zwischen Wald und Atmosphäre. Ausserdem ist sie ein Indikator für die Widerstandskraft des Waldes gegen äussere Einflüsse, um das Potential für den Erhalt der Biodiversität und um Limitierungen für die Waldverjüngung abzuschätzen. Obwohl die Struktur des Waldes keine direkt messbare Grösse ist, können einzelne Komponenten der Waldstruktur durch eine Vielzahl an Strukturvariablen beschrieben werden. Für eine flächendeckende Ableitung von Waldstrukturvariablen ist flugzeuggestütztes Laserscanning, eine der aktuellsten Erdbeobachtungstechnologien, besonders geeignet. In der vorliegenden Dissertation wurde die Eignung von flugzeugestütztem Laserscanning für eine automatisierte und exakte Charakterisierung der Waldstruktur in gemässigten Mischwäldern untersucht. Es wurden Methoden für eine objektive Ableitung von Waldstrukturvariablen entwickelt und verbessert, insbesondere im Hinblick auf das Anwendungspotential für die Waldinventur und -wirtschaft, Waldökologie und Strahlungstransfermodellierung. In diesem Zusammenhang wurden sowohl die spezifischen Eigenschaften der Laserscanningdaten als auch die Bedeutung der räumlichen Einheit für die Ableitung von Waldstrukturvariablen untersucht. Es wurde ein neues, datengetriebenes Konzept der Waldstrukturtypen (CSTs) entwickelt, welches sowohl Eigenschaften der physikalisch orientierten Ansätze auf Einzelbaumebene und als auch die der flächenbasierten, empirischen Ansätze vereint: objektive Kriterien, eine geringe Skalenabhängikeit und Berücksichtigung der spezifischen Dateneigenschaften. Die Vorteile und Einschränkungen dieser Ergebnisse werden diskutiert und Vorschläge für mögliche Verbesserungen und zukünftige Entwicklungen präsentiert. Dabei wird insbesondere auf die Schwierigkeit der Etablierung eines neuen Konzeptes zur Waldstrukturcharakterisierung in den jeweiligen Anwendungsbereichen eingegangen.

Table of contents

Chapter 1

Introduction

Chapter 2

Operational forest structure monitoring using

1

23

airborne laser scanning

Chapter 3

Towards automated characterization of canopy

43

layering in mixed temperate forests using airborne laser scanning

Chapter 4

Forest canopy-structure characterization: A data-

73

driven approach

Chapter 5

Synopsis

113

Curriculum vitae

133

List of publications

135

Acknowledgements

141

Chapter

1 Introduction

1

Introduction

1.1 Background “The tree which moves some to tears of joy is, in the eyes of others, only a green thing which stands in the way.” - William Blake

Forests are the dominant and one of the most biologically diverse terrestrial ecosystems on Earth (Pan et al. 2013). They cover approximately one third of Earth’s total land area (FAO 2010) and play a pivotal role in global biogeochemical and biophysical cycles (Ross 2012, Bonan 2008, Betts et al. 2001), accounting for 75% of terrestrial gross primary production and 80% of Earth’s plant biomass (Beer et al. 2010, Kindermann et al. 2008). Moreover, they are major contributors to maintaining terrestrial biodiversity (Pan et al. 2013) and provide a wide range of valuable ecosystem goods and services, including food, timber and climate moderation (Jackson et al. 2005, McKinley et al. 2011). Understanding and monitoring forest ecosystems and their underlying processes allows projection of changes in biogeochemical and biophysical cycles under, for example, changing climate conditions and supports forest management, conservation biology and ecological restoration (Pan et al. 2013, Jonsson & Wardle 2010, Sierra et al. 2009, Purves & Pacala 2008).

1.1.1 Canopy structure Forest ecosystems are structurally complex, three-dimensional systems and consist of various vertical and horizontal structural elements arranged in specific spatial patterns, hereafter summarized as 'canopy structure' (Pan et al. 2013, Nadkarni et al. 2008, Disney et al. 2006). This canopy structure is considered a particularly crucial constituent of forest ecosystem functions and processes as it influences the energy flux between atmosphere and forests (Xue et al. 2011, Shugart et al. 2010, Yang & Friedl 2003, Saunders et al. 1998), serves as an indicator for determining forest stand resistance to disturbances (Kayes & Tinker 2012), enables estimation of the potential for

2

Chapter 1

conservation of biodiversity (Graf et al. 2009, Lindenmayer et al. 2006) and allows the identification of recruitment limitations (Herrera & García 2010, Spies 1998). Canopy structure itself is not a measurable quantity but properties of the canopy structure can be described by means of a wide variety of canopy structure variables (McElhinny et al. 2005). Canopy structure variables cover both individual tree- and canopy-related variables. Individual tree variables include tree height, diameter at breast height, crown length, height to the crown base, stem volume, biomass and crown projection area, whereas canopy-related variables include mean canopy height, gap fraction, canopy volume and fractional canopy cover (Lefsky 2010, Saatchi et al. 2011, Nadkarni et al. 2008, McElhinny et al. 2005, Lindberg & Hollaus 2012, Wing et al. 2012, Hilker et al. 2010, Zhao et al. 2011). Traditionally, canopy structure variables are assessed by conventional fieldwork in relatively small sampling areas, which is time-consuming and occasionally subjective (Foody 2010, Haara & Leskinen 2009, McElhinny et al. 2005). Advances in Earth Observation (EO) systems and analysis techniques have greatly improved the ability to measure canopy structure variables over large areas, not only in the horizontal, but also in the vertical dimension (Asner et al. 2012, Jones et al. 2012, Saatchi et al. 2011, Hall et al. 2011, Koch 2010, Roberts et al. 2007). The derivation of those canopy structure variables using EO is carried out individually for smaller spatial units, which are pre-defined sub-areas of the full area of interest.

1.1.2 Spatial units for canopy structure characterization The characterization of canopy structure using EO can be performed on two different ways: i) based on individual tree or tree crown detection/delineation (ITD/ITC) (Vastaranta et al. 2012, Kaartinen et al. 2012, Larsen et al. 2011, van Leeuwen & Nieuwenhuis 2010), and ii) based on, in most cases, regular spatial units with predefined dimensions (Vastaranta et al. 2012, Latifi et al. 2012, Villikka et al. 2012, Hill & Thomson 2005, Couteron et al. 2005). Both approaches have specific advantages and disadvantages (Wulder et al. 2013, Vastaranta et al. 2012). Therefore, an acceptable compromise needs to be found between the technical capabilities of EO data and user

3

Introduction

demands such as the application domain or cost–benefit considerations (Treitz et al. 2012, Zimble et al. 2003). Figure 1.1 shows an example of forest patch (left panel), its ITD/ITC-based representation containing detected individual trees and their delineated crowns (middle panel), and its area-based representation by a voxel-grid, classified into ranges of the height above ground (right panel).

Figure 1.1 Concepts of canopy structure representation for a given forest patch (left panel): representation of the individual tree/tree crown level (middle panel), and area-based representation using a voxel-grid, coloured by height above ground (right panel).

The main advantage of ITD/ITC is that it provides true stem distribution series, one of the key variables for forest inventory/ forest management (Kaartinen et al. 2012). Nevertheless, the discrimination of single trees using ITD/ITC approaches is a problem of varying complexity, depending on the density of the forest and the spatial resolution of EO data, and often leads to an underestimation of the number of detected trees/tree crowns (Vastaranta et al. 2011, Falkowski et al. 2008). Thus, the practical use of ITD/ITC needs spatially very highly resolved EO data, which adds to the costs and the amount of data that would need to be stored. The advantages of an area-based assessment of canopy structure are the wall-towall measures of canopy structure variables and modelled attributes (e.g. stem volume, biomass and basal area), the scalability of the resulting information and the reduced dependency on the spatial resolution of EO data (White et al. 2013). In contrast, EO metrics generated using this approach are known to be strongly intercorrelated and the accuracy of predictively modelled canopy structure variables in complex stands is insufficient (Bouvier et al. 2015, Chen et al. 2013, Khan et al. 2007).

4

Chapter 1

For ITD/ITC approaches, the basic spatial unit is given by the horizontal and vertical tree/tree crown dimension, with possible upscaling to plot, stand, landscape, forest or regional scale (Kaartinen et al. 2012, van Leeuwen & Nieuwenhuis 2010). The pre-defined spatial units used by the area-based characterization can be regularly spaced grids or irregular objects such as micro-stand/stand polygons or administrative borders (Vastaranta et al. 2012, Shugart et al. 2010, Næsset 2004). The use of regularly spaced grids instead of irregular objects favours a better intercomparison and statistical analysis of the calculated canopy structure variables (as the spatial subset is always the same) and enables spatial up- and downscaling in a robust and transparent manner (cf. Leiterer et al. 2015b, Pascual et al. 2008, van Aardt et al. 2006). For practical use, the derived canopy structure information on the regularly spaced grids can be easily aggregated afterwards to plot, stand or landscape scale, either using segmentation algorithms or based on given boundaries. The choice of the specific spatial unit in areabased assessments of canopy structure crucially impacts the structure analysis, as a decrease in horizontal resolution will result in an increasing mixture of horizontal and vertical structure elements within each spatial unit (Frazer et al. 2005, Staebler & Fitzjarrald 2005). The selection of either of the two approaches or a possible combination of both to derive canopy structure variables is currently mainly driven by availability of field reference data and/or user requirements and is usually limited by the features present in the available data (Vastaranta et al. 2012, Breidenbach et al. 2010). Nevertheless, most canopy structure components also have inherent spatial scales and the applied spatial unit should be chosen according to the respective scale of the investigated structural component (Pasher & King 2011, Shugart et al. 2010, Saunders et al. 2005, Holling 1992).

5

Introduction

1.2 Airborne laser scanning for canopy structure characterization Most passive optical remote sensing approaches lack information about the vertical dimension (Jones et al. 2012, Hall et al. 2011, Roberts et al. 2007). In contrast, light detection and ranging (LiDAR) systems are suitable to provide not only horizontal information about the canopy structure, but also detailed vertical information based on the physical measurement principles of active sensing and canopy gap fraction (Kane et al. 2010, Næsset 2002). The physical principle consists of the emitted laser pulse and the measured two-way runtime from the LiDAR system to the Earth surface (Baltsavias 1999). A distinction is made between discrete return scanning systems, designed to record either one backscattered echo (e.g. first or last) or multiple echoes (e.g. first, last and/or intermediate echo) per emitted pulse, and full-waveform systems, designed to approximate the entire returned signal by digitization (Mallet & Bretar 2009, Lim et al. 2003). The shape of the waveform is, inter alia, influenced by the orientation of the reflection objects (e.g. slope), the material properties of the reflectors (e.g. level of absorption/reflection, roughness) and the spatial (vertical) distribution of the reflecting objects (Mallet & Bretar 2009, Wagner et al. 2006). A decomposition of the full waveform enables representative echo descriptions including amplitude, width and intensity of each echo (Heinzel & Koch 2011) (Figure 1.2).

Figure 1.2 Physical concept of airborne laser scanning: interaction of the emitted laser pulse with the vegetation canopy (left panel), full and decomposed waveform (middle panel), and the resulting echo descriptors (right panel).

6

Chapter 1

An additional attribute for describing LiDAR systems is the size range of the footprint. The footprint is defined as the sampling area on the ground, whose size and shape depend on the scanning geometry, the topography and the flying altitude (Hyyppä et al. 2008, Sheng 2008). LiDAR systems with a range of footprint sizes from 10 to 70 m in diameter are defined as large-footprint systems, whereas small-footprint systems cover an area on the ground between 0.2 and 3.0 m in diameter (Mallet & Bretar 2009, Hyyppä et al. 2008). In particular, small-footprint airborne laser scanner (ALS) systems enable the derivation of canopy structure variables with a high spatial resolution. Numerous studies have shown that such derived canopy structure variables are well correlated with field measurements, which suggests the possibility of using small-footprint ALS more broadly to support forest inventory and management as well as to improve our understanding of the structural complexity of forests (Wulder et al. 2012, van Leeuwen & Nieuwenhuis 2010, Popescu et al. 2003, Dubayah & Drake 2000). Although ALSderived information can comprehensively characterise the vertical canopy structure (Muss et al. 2011, Wang et al. 2008, Lovell et al. 2003), this not necessarily means that they can completely represent the actual canopy structure (Morsdorf et al. 2009, Lee et al. 2004). Assessing canopy structure using ALS includes methods based on the vertical stratification of the canopy (Ferraz et al. 2012, Lindberg et al. 2012, Morsdorf et al. 2010) and the interpretation of the horizontal patterns of strata (Nieschulze et al. 2012, Zhao et al. 2009, Maltamo et al. 2005) as well as ITD/ITC methods (Kaartinen et al. 2012, Ene et al. 2012, Hyyppä et al. 2008, Popescu 2007, Morsdorf et al. 2004). According to Maltamo et al. (2014), Wulder et al. (2013) and Pascual et al. (2013), two main approaches for deriving canopy structure variables using ALS can be distinguished: (I) The first type, the so-called area based approaches (ABAs), is based on the processing of ALS data to calculate discrete metrics (e.g. height and intensity percentiles or variability metrics) within pre-defined areas such as established field plots or grid cells (Zhao et al. 2009, Næsset 2004). In combination with field measurements, the derived LiDAR-metrics can be used afterwards as predictor variables to estimate a wide range of canopy structure attributes (Maltamo et al. 2014), where the canopy structure variables measured in the field and the LiDAR-metrics are dependent and independent variables, respectively (cf. White et al. 2013, Holopainen et

7

Introduction

al. 2010, Næsset 2002). Numerous investigations for characterizing the relationship between LiDAR-metrics and field measurements have been reported: Means et al. (2000), for instance, used linear models, Andersen et al. (2011) and Latifi et al. (2011) applied non-parametric approaches, and Breidenbach & Kublin (2009) and Junttila et al. (2008) investigated Bayesian methods. Area-based approaches have been at the operational stage for several years (cf. Wulder et al. 2013), however, there are still limitations: the pulse density of ALS data should be large enough to produce reliable estimated of canopy structure variables, most of the common models describing the relationship between LiDAR metrics and field measurements are scale-dependent, the derived canopy structure variables are restricted to canopy structure variables that can be measured in the field, and often a large set of site-specific considerations and assumptions must be made (Maltamo et al. 2014, Gleason & Im 2012, Zhao et al. 2009). Even if the term ABA is often used in literature to describe the two-stage procedure of i) calculating LiDAR metrics, and ii) building up relationships between these metrics and field measurements (cf. Asner et al. 2012, Naesset 2007), we used in this thesis the term ABA to describe only the spatial frame, at which canopy structure is assessed. (II) The second, more physical type of approaches focuses on individual tree detection and segmentation (ITD/ITC), which allows direct measurements of variables such as tree height or crown size (Hyyppä et al. 2008). These variables can be used afterwards with allometric models to predict additional structure variables such as normal section or biomass (Duncanson et al. 2015, Persson et al. 2002). In this context, an important consideration is that usually not all trees can be detected, whereby the degree of successful detection is on the hand driven by the detection algorithm and its parameterization (Kaartinen et al. 2012) and on the other hand affected by the specific forest conditions (Vauhkonen et al. 2012). Common tree detection methods rely on raster-based canopy height models (Popescu et al. 2003, Persson et al. 2002) or use approaches based on the ALS point cloud, in particular if a segmentation of the tree crown should be achieved (Leiterer et al. 2013, Solberg et al. 2006, Morsdorf et al. 2004). The disadvantage of these methods is the requirement on the ALS data, i.e. the need for higher point densities, in particular for segmentation approaches (Wulder et al 2008, Morsdorf et al. 2004, Brandtberg et al. 2003). Moreover, many tree segmentation algorithms are limited in the detection and segmentation of grouped or understory trees, in particular in dense forests (Vauhkonen et al. 2012, Zhao et al. 2009). Beside

8

Chapter 1

this, the use of allometric models poses problems as well, because allometric relationships can be influenced by site-specific factors such as silvicultural history (Maltamo et al. 2007, Korpela et al 2004). In addition, in some studies the complementary properties of ITD/ITC and ABAs were used. Lindberg et al. (2010) and Maltamo et al. (2004), for example, found the combination of ITD/ITC and ABAs beneficial for improving tree detection, Breidenbach et al. (2010) proposed the use of individual tree detection methods to support the generation of field reference data, and Vauhkonen et al. (2014) used information generated from an ABA for targeting detailed analyses on the individual tree level.

9

Introduction

1.3 Research needs and research questions Shugart et al. (2010), van Leeuwen & Nieuwenhuis (2010) and Spanos & Feest (2007) pointed out the high potential of remote sensing data and techniques for direct measurements of canopy structure on multiple scales as well as the need for further development of indicator schemes to link canopy structure components to forest ecosystem goods and services which are not directly measurable. Wulder et al. (2013) and White et al. (2013) noted that ALS based methods, in particular, are in a robust and operational stage to assess the canopy structure. Existing ALS based approaches, however, often require a substantial amount of prior information and results are frequently dependent on the particular ALS data set configuration, pre-defined spatial units and the forest metrics used (Wulder et al. 2012, Treitz et al. 2012, Ferraz et al. 2012, Zhao et al. 2011, Korpela et al. 2010). Wulder et al. (2012), for example, concluded that the primary motivation for using ALS is “...to emulate ground plots, acknowledging that some ground data is needed to calibrate the LiDAR measures”, which highlights the common practice of aligning ALS data and ALS based methods to traditional forest inventory practices, with little regard to ALS data properties and actual canopy structure. Moreover, the existing approaches often rely on manual processing steps, again benefiting from prior information about stand characteristics such as tree species, tree age or management type (Wulder et al. 2012). Thus, most of these approaches have limited transferability to other sites, due to inherent local calibration of the model used, and their comparability is limited. In addition, advanced LiDAR metrics could help estimate of important canopy structure variables which are difficult to measure in the field and thus are not included in the set of variables of traditional forest inventories such as, for example, canopy layering, tree architecture and foliage distribution. Advanced LiDAR metrics, however, do not necessarily correspond to existing groupings and/or classifications of canopy structure into thematic units such as those used in forest management (e.g. classes of forest development stages) or forest ecology (e.g. habitat types). Consequently, for the application of those types of LiDAR metrics, a compromise needs to be determined: on one hand, it is necessary to link canopy structure variables based on ground measurements and advanced LiDAR metrics. On the other hand, differences in the semantic descriptions of canopy structure variables and differences in the spatial scales

10

Chapter 1

used to derive the canopy structural variables need to be considered. Area-based LiDAR metrics, in particular, are sensitive to underlying spatial units and ALS data properties, which need to be considered and whose effects should be examined in depth. Accordingly, there is a need for more comprehensive methods for characterizing and monitoring the canopy structure within an objective framework. This means bridging the gap between information contained in the ALS data and established concepts of users/practitioners. In this context, an improved understanding of the influence of ALS data properties on canopy structure analysis in combination with an improved understanding of the influence of spatial scales on canopy structure analysis is required. This includes evaluating multi-seasonal ALS data with very high point densities for canopy structure characterization and finding a comprehensive set of physically derivable canopy structure variables beyond usual forest parameters.

11

Introduction

1.3.1 Research questions Motivated by the research needs outlined above, the overall objective of this thesis is to develop methods that advance canopy structure characterization and monitoring using multi-seasonal, small-footprint ALS data. The three research questions are:

1) How can we extract information about the canopy structure using ALS as comprehensively as possible while being physically accurate by combining ITD/ITC and ABA?

2) How can we use ALS data to characterise canopy layering and foliage distribution on a regional scale in an automatic way and with as little as possible use of ancillary information?

3) How and to what extent can we describe the continuous canopy structure using the data-driven, multi-scale concept of canopy structure types, considering the properties of ALS data?

These research questions are addressed in the three peer-reviewed scientific publications contained in this thesis:

1) “Operational forest structure monitoring using airborne laser scanning” (Leiterer et al. 2013)

2) “Towards automated characterization of canopy layering in mixed temperate forests using airborne laser scanning” (Leiterer et al. 2015a)

3) “Forest canopy-structure characterization: A data-driven approach” (Leiterer et al. 2015b)

12

Chapter 1

1.4 Thesis outline The core of this thesis is a series of three chapters, each of them addressing one of the research questions. Figure 1.3 provides an overview of these core chapters.

Figure 1.3 Overview of and links between the three core chapters of this thesis.

Chapter 2 describes the advancements of methods for canopy structure characterization to allow semi-automatic use in forest inventory/forest management. We outline the derivation of a variety of canopy structure variables by combining ITD/ITC and ABA, meeting stakeholder’s requirements in terms of accuracy, spatial resolution and information content. Chapter 3 discusses the development of a method to characterise canopy layering and foliage distribution as important elements of forest ecosystems dynamics and habitat preference on a regional level. To estimate the application potential on larger scales, the transferability of the method is tested. Moreover, we investigate the added value of multi-seasonal ALS data and the influence of reference data and ALS data properties on the accuracy assessment. Chapter 4 describes the development of an automatic, data-driven approach to derive structural homogenous areas, here called canopy structure types (CSTs). We assess scaling effects and the influence of ALS data properties on CSTs and evaluate the applicability of the CST-concept as an automated and objective framework. In Chapter 5, we synthesize the main results presented in chapters 2–4 and provide concluding remarks and an outlook.

13

Introduction

1.5 References Asner, G.P., Mascaro, J., Muller-Landau, H.C., Vieilledent, G., Vaudry, R., Rasamoelina, M., Hall, J.S., & van Breugel, M. (2012). A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia, 168 (4), 1147-1160 Andersen, H.-E., Strunk, J., Temesgen, H., Atwood, D., & Winterberger, K. (2011). Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: A case study in the boreal forests of interior Alaska. Canadian Journal of Remote Sensing, 37 (6), 596-611 Baltsavias, E.P. (1999). Airborne laser scanning : basic relations and formulas. ISPRS Journal of Photogrammetry and Remote Sensing, 54 (2-3), 199-214 Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M.A., Baldocchi, D., Bonan, G.B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K.W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F.I., & Papale, D. (2010). Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate. Science, 329 (5993), 834-838 Betts, A.K., Ball, J.H., & McCaughey, J.H. (2001). Near-surface climate in the boreal forest. Journal of Geophysical Research D: Atmospheres, 106 (D24), 33529-33541 Bonan, G.B. (2008). Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320 (5882), 1444-1449 Bouvier, M., Durrieu, S., Fournier, R.A., & Renaud, J.-P. (2015). Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sensing of Environment, 156, 322-334 Brandtberg, T., Warner, T.A., Landenberger, R.E., & McGraw, J.B. (2003). Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density LiDAR data from the eastern deciduous forest in North America. Remote Sensing of Environment, 85 (3), 290303 Breidenbach, J., Næsset, E., Lien, V., Gobakken, T., & Solberg, S. (2010). Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sensing of Environment, 114 (4), 911-924 Breidenbach, J., & E. Kublin, E. (2009). Estimating Timber Volume using Airborne Laser Scanning Data based on Bayesian Methods. IUFRO Division 4 meeting: Extending Forest Inventory and Monitoring over Space and Time. 1922, May 2009 Quebec City, Canada. Chen, J.M., Chen, X., & Ju, W. (2013). Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity. Biogeosciences, 10 (7), 4879-4896

14

Chapter 1

Couteron, P., Pelissier, R., Nicolini, E.A., & Paget, D. (2005). Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images. Journal of Applied Ecology, 42 (6), 1121-1128 Disney, M., Lewis, P., & Saich, P. (2006). 3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains. Remote Sensing of Environment, 100 (1), 114-132 Dubayah, R.O., & Drake, J.B. (2000). LiDAR remote sensing for forestry. Journal of Forestry, 98 (6), 44-52 Duncanson, L.I., Dubayah, R.O., Cook, B.D., Rosette, J., & Parker, G. (2015). The importance of spatial detail: Assessing the utility of individual crown information and scaling approaches for LiDAR-based biomass density estimation. Remote Sensing of Environment, 168, 102-112 Ene, L., Næsset, E., & Gobakken, T. (2012). Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates. International Journal of Remote Sensing, 33 (16), 5171-5193 Falkowski, M.J., Smith, A.M.S., Gessler, P.E., Hudak, A.T., Vierling, L.A., & Evans, J.S. (2008). The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using LiDAR data. Canadian Journal of Remote Sensing, 34 (SUPPL. 2), S338-S350 FAO (Food and Agriculture Organization of the United Nations) (2010). Global forest resources assessment 2010: main report. Food and Agriculture Organization of the United Nations, ISBN:978-92-5-106654-6, ISSN:0258-6150, Rome, 2010 Ferraz, A., Bretar, F., Jacquemoud, S., Gonçalves, G., Pereira, L., Tomé, M., & Soares, P. (2012). 3-D mapping of a multi-layered Mediterranean forest using ALS data. Remote Sensing of Environment, 121, 210-223 Frazer, G.W., Wulder, M.A., & Niemann, K.O. (2005). Simulation and quantification of the finescale spatial pattern and heterogeneity of forest canopy structure: A lacunarity-based method designed for analysis of continuous canopy heights. Forest Ecology and Management, 214 (1-3), 65-90 Foody, G.M. (2010). Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sensing of Environment, 114 (10), 2271-2285 Graf, R., Mathys, L., & Bollmann, K. (2009). Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps. Forest Ecology and Management, 257 (1), 160-167 Gleason, C.J., & Im, J. (2012). Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment, 125, 80-91 Haara, A., & Leskinen, P. (2009). The assessment of the uncertainty of updated stand-level inventory data. Silva Fennica, 43 (1), 87-112

15

Introduction

Hall, F.G., Bergen, K., Blair, J.B., Dubayah, R., Houghton, R., Hurtt, G., Kellndorfer, J., Lefsky, M., Ranson, J., Saatchi, S., Shugart, H.H., & Wickland, D. (2011). Characterizing 3D vegetation structure from space: Mission requirements. Remote Sensing of Environment, 115 (11), 2753-2775 Heinzel, J., & Koch, B. (2011). Exploring full-waveform LiDAR parameters for tree species classification. International Journal of Applied Earth Observation and Geoinformation, 13 (1), 152-160 Herrera, J.M., & García, D. (2010). Effects of forest fragmentation on seed dispersal and seedling establishment in ornithochorous trees. Conservation Biology, 24 (4), 1089-1098 Hilker, T., van Leeuwen, M., Coops, N.C., Wulder, M.A., Newnham, G.J., Jupp, D.L.B., & Culvenor, D.S. (2010). Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand. Trees - Structure and Function, 24 (5), 819832 Hill, R.A., & Thomson, A.G. (2005). Mapping woodland species composition and structure using airborne spectral and LiDAR data. International Journal of Remote Sensing, 26 (17), 3763-3779 Holling, C.S. (1992). Cross-scale morphology, geometry, and dynamics of ecosystems. Ecological Monographs, 62, 447-502 Holopainen, M., Vastaranta, M., Rasinmäki, J., Kalliovirta, J., Mäkinen, A., Haapanen, R., Melkas, T., Yu, X., & Hyyppä, J. (2010). Uncertainty in timber assortment estimates predicted from forest inventory data. European Journal of Forest Research, 129 (6), 1131-1142 Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X., & Maltamo, M. (2008). Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing, 29 (5), 1339-1366 Jackson, R.B., Jobbágy, E.G., Avissar, R., Roy, S.B., Barrett, D.J., Cook, C.W., Farley, K.A., Le Maitre, D.C., McCarl, B.A., & Murray, B.C. (2005). Atmospheric science: Trading water for carbon with biological carbon sequestration. Science, 310 (5756), 1944-1947 Jones, T.G., Coops, N.C., & Sharma, T. (2012). Assessing the utility of LiDAR to differentiate among vegetation structural classes. Remote Sensing Letters, 3 (3), 231-238 Jonsson, M., & Wardle, D.A. (2010). Structural equation modelling reveals plant-community drivers of carbon storage in boreal forest ecosystems. Biology Letters, 6 (1), 116-119 Junttila, V., Maltamo, M., & Kauranne, T. (2008). Sparse Bayesian estimation of forest stand characteristics from air-borne laser scanning. Forest Science, 54 (5), 543-552 Kaartinen, H., Hyyppä, J., Yu, X., Vastaranta, M., Hyyppä, H., Kukko, A., Holopainen, M., Heipke, C., Hirschmugl, M., Morsdorf, F., Næsset, E., Pitkänen, J., Popescu, S., Solberg, S., Wolf, B.M., & Wu, J.C. (2012). An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sensing, 4 (4), 950-974

16

Chapter 1

Kane, R., Bakker, J.D., McGaughey, R.J., Lutz, J.A., Gersonde, R.F., & Franklin, J.F. (2010). Examining conifer canopy structural complexity across forest ages and elevations with LiDAR data. Canadian Journal of Forest Research, 40 (4), 774-787 Kayes, L.J., & Tinker, D.B. (2012). Forest structure and regeneration following a mountain pine beetle epidemic in southeastern Wyoming. Forest Ecology and Management, 263, 57-66 Khan, J.A., Van Aelst, S., & Zamar, R.H. (2007). Robust linear model selection based on least angle regression. Journal of the American Statistical Association, 102 (480), 1289-1299 Kindermann, G.E., McCallum, I., Fritz, S., & Obersteiner, M. (2008). A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fennica, 42 (3), 387-396 Koch, B. (2010). Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 65 (6), 581-590 Korpela, I., Ørka, H.O., Maltamo, M., Tokola, T., & Hyyppä, J. (2010). Tree species classification using airborne LiDAR - effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fennica, 44 (2), 319-339 Korpela, I. (2004). Individual tree measurements by means of digital aerial photogrammetry. Silva Fennica Monographs, 3, 1-93 Larsen, M., Eriksson, M., Descombes, X., Perrin, G., Brandtberg, T., & Gougeon, F.A. (2011). Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions. International Journal of Remote Sensing, 32 (20), 5827-5852 Latifi, H., Fassnacht, F., & Koch, B. (2012). Forest structure modeling with combined airborne hyperspectral and LiDAR data. Remote Sensing of Environment, 121, 10-25 Latifi, H., Nothdurft, A., & Koch, B. (2010). Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: Application of multiple optical/LiDARderived predictors. Forestry, 83 (4), 395-407 Lee, A.C., Lucas, R.M., & Brack, C. (2004). Quantifying vertical forest stand structure using small footprint LiDAR to assess potential stand dynamics. International Archive of Photogrammetry, Remote Sensing and Spatial Information Sciences 2004, 36 (8/W2), 213-217 Leiterer, R., Furrer, R., Schaepman, M.E., & Morsdorf, F. (2015a). Towards automated characterization of canopy layering in mixed temperate forests using airborne laser scanning. Forests, 6 (11), 4246-4167 Leiterer, R., Furrer, R., Schaepman, M.E., & Morsdorf, F. (2015b). Forest canopy-structure characterization: A data-driven approach. Forest Ecology and Management, 358, 48-61

17

Introduction

Leiterer, R., Muecke, W., Morsdorf, F., Hollaus, M., Pfeifer, N. & Schaepman, M.E. (2013). Operational forest structure monitoring using airborne laser scanning [Flugzeuggestütztes Laserscanning für ein operationelles Waldstrukturmonitoring]. Photogrammetrie, Fernerkundung, Geoinformation, 2013 (3), 173-184 Lefsky, M.A. (2010). A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophysical Research Letters, 37 (15), art. no. L15401 Lim, K., Hopkinson, C., & Treitz, P. (2008). Examining the effects of sampling point densities on laser canopy height and density metrics. Forestry Chronicle, 84 (6), 876-885 Lindberg, E. & Hollaus, M. (2012). Comparison of methods for estimation of stem volume, stem number and basal area from airborne laser scanning data in a hemi-boreal forest. Remote Sensing, 4 (4), 1004-1023 Lindberg, E., Olofsson, K., Holmgren, J., & Olsson, H. (2012). Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data. Remote Sensing of Environment, 118, 151-161 Lindberg, E., Holmgren, J., Olofsson, K., Wallerman, J., & Olsson, H. (2010). Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods. International Journal of Remote Sensing, 31 (5), 1175-1192 Lindenmayer, D.B., Franklin, J.F., & Fischer, J. (2006). General management principles and a checklist of strategies to guide forest biodiversity conservation. Biological Conservation, 131 (3), 433-445 Lovell, J.L., Jupp, D.L.B., Culvenor, D.S., & Coops, N.C. (2003). Using airborne and groundbased ranging LiDAR to measure canopy structure in Australian forests. Canadian Journal of Remote Sensing, 29, 607-622 Mallet, C., & Bretar, F. (2009). Full-waveform topographic LiDAR: State-of-the-art. ISPRS Journal of Photogrammetry and Remote Sensing, 64 (1), 1-16 Maltamo, M., Næsset, E., & Vauhkonen, J. (eds.) (2014). Forestry applications of airborne laser scanning: Concept and case studies. Managing Forest Ecosystems, 27, Springer Science + Business Media Dordrecht 2014, DOI 10.1007/978-94-017-8663-8_1. Maltamo, M. , Packalén, P. , Peuhkurinen, J. , Suvanto, A. , Pesonen, A., & Hyyppä, J. (2007). Experiences and possibilities of ALS based forest inventory in Finland. ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, ISPRS, Volume XXXVI, Espoo, Finland, 270-279 Maltamo, M., Packalén, P., Yu, X., Eerikäinen, K., Hyyppä, J., & Pitkänen, J. (2005). Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data. Forest Ecology and Management, 216 (1-3), 41-50

18

Chapter 1

Maltamo, M., Eerikäinen, K., Pitkänen, J., Hyyppä, J., & Vehmas, M. (2004). Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment, 90 (3), 319-330 McElhinny, C., Gibbons, P., Brack, C., & Bauhus, J. (2005). Forest and woodland stand structural complexity: Its definition and measurement. Forest Ecology and Management, 218 (1-3), 1-24 McKinley, D.C., Ryan, M.G., Birdsey, R.A., Giardina, C.P., Harmon, M.E., Heath, L.S., Houghton, R.A., Jackson, R.B., Morrison, J.F., Murray, B.C., Pataki, D.E., & Skog, K.E. (2011). A synthesis of current knowledge on forests and carbon storage in the United States. Ecological Applications, 21 (6), 1902-1924 Means, J.E., Acker, S.A., Fitt, B.J., Renslow, M., Emerson, L., & Hendrix, C.J. (2000). Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering and Remote Sensing, 66 (11), 1367-1371 Morsdorf, F., Mårell, A., Koetz, B., Cassagne, N., Pimont, F., Rigolot, E., & Allgöwer, B. (2010). Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning. Remote Sensing of Environment, 114 (7), 1403-1415 Morsdorf, F., Nichol, C., Malthus, T., & Woodhouse, I.H. (2009). Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling. Remote Sensing of Environment, 113, 2152-2163 Morsdorf, F., Meier, E., Kötz, B., Itten, K.I., Dobbertin, M., & Allgöwer, B. (2004). LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sensing of Environment, 92 (3), 353-362 Muss, J.D., Mladenoff, D.J., & Townsend, P.A. (2011). A pseudo-waveform technique to assess forest structure using discrete LiDAR data. Remote Sensing of Environment, 115, 824-835 Nadkarni, N.M., McIntosh, A.C.S., & Cushing, J.B. (2008). A framework to categorize forest structure concepts. Forest Ecology and Management, 256 (5), 872-882 Naesset, E. (2007). Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia. Scandinavian Journal of Forest Research, 22 (5), 433-442 Næsset, E. (2004). Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scandinavian Journal of Forest Research, 19 (2), 164-179 Næsset, E. (2002). Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 80 (1), 88-99 Nieschulze, J., Zimmermann, R., Börner, A., & Schulze, E.-D. (2012). An assessment of forest canopy structure by LiDAR: Derivation and stability of canopy structure parameters across forest management types. Forstarchiv, 83 (6), 95-209

19

Introduction

Pan, Y., Birdsey, R.A., Phillips, O.L., & Jackson, R.B. (2013). The structure, distribution, and biomass of the world's forests. Annual Review of Ecology, Evolution, and Systematics, 44, 593-622 Pascual, C., García-Abril, A., García-Montero, L.G., Martín-Fernández, S., & Cohen, W.B. (2008). Object-based semi-automatic approach for forest structure characterization using LiDAR data in heterogeneous Pinus sylvestris stands. Forest Ecology and Management, 255 (11), 3677-3685 Pascual, C., Mauro, F., Hernando, A., & Martín-Fernández, S. (2013). Inventory techniques in participatory forest management. In: Quantitative techniques in participatory forest management. CRC Press, Taylor& Francis Group: Boca Raton, Florida, United States. Pasher, J., & King, D.J. (2011). Development of a forest structural complexity index based on multispectral airborne remote sensing and topographic data. Canadian Journal of Forest Research, 41 (1), 44-58 Persson, Å., Holmgren, J., & Söderman, U. (2002). Detecting and measuring individual trees using an airborne laser scanner. Photogrammetric Engineering and Remote Sensing, 68 (9), 925-932 Popescu, S.C. (2007). Estimating biomass of individual pine trees using airborne LiDAR. Biomass and Bioenergy, 31, 646-655 Popescu, S.C., Wynne, R.H., & Nelson, R.F. (2003). Measuring individual tree crown diameter with LiDAR and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing, 29 (5), 564-577 Purves, D., & Pacala, S. (2008). Predictive models of forest dynamics. Science, 320 (5882), 14521453 Roberts, J., Tesfamichael, S., Gebreslasie, M., Van Aardt, J., & Ahmed, F. (2007). Forest structural assessment using remote sensing technologies: an overview of the current state of the art. Southern Hemisphere Forestry Journal, 69 (3), 183-203 Ross, A.N. (2012). Boundary-layer flow within and above a forest canopy of variable density. Quarterly Journal of the Royal Meteorological Society, 138 (666), 1259-1272 Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, M., Mitchard, E.T.A., Salas, W., Zutta, B.R., Buermann, W., Lewis, S.L., Hagen, S., Petrova, S., White, L., Silman, M., & Morel, A. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences of the United States of America, 108 (24), 9899-9904 Saunders, S.C., Chen, J., Drummer, T.D., Gustafson, E.J., & Brosofske, K.D. (2005). Identifying scales of pattern in ecological data: A comparison of lacunarity, spectral and wavelet analyses. Ecological Complexity, 2 (1), 87-105

20

Chapter 1

Saunders, S.C., Chen, J., Crow, T.R., & Brosofske, K.D. (1998). Hierarchical relationships between landscape structure and temperature in a managed forest landscape. Landscape Ecology, 13 (6), 381-395 Sheng, Y. (2008). Quantifying the size of a LiDAR footprint: A set of generalized equations. IEEE Geoscience and Remote Sensing Letters, 5 (3), 419-422 Shugart, H.H., Saatchi, S., & Hall, F.G. (2010). Importance of structure and its measurement in quantifying function of forest ecosystems. Journal of Geophysical Research, 115 (4), 1-16 Sierra, C.A., Loescher, H.W., Harmon, M.E., Richardson, A.D., Hollinger, D.Y., & Perakis, S.S. (2009). Interannual variation of carbon fluxes from three contrasting evergreen forests: the role of forest dynamics and climate. Ecology, 90 (10), 2711-2723 Solberg, S., Naesset, E., & Bollandsas, O.M. (2006). Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest. Photogrammetric Engineering and Remote Sensing, 72 (12), 1369-1378 Spanos, K.A., & Feest, A. (2007). A review of the assessment of biodiversity in forest ecosystems. Management of Environmental Quality, 18 (4), 475-486 Spies, T.A. (1998). Forest structure: a key to the ecosystem. Northwest Science, 72 (2), 34-36 Staebler, R.M., & Fitzjarrald, D.R. (2005). Measuring canopy structure and the kinematics of subcanopy flows in two forests. Journal of Applied Meteorology, 44 (8), 1161-1179 Treitz, P., Lim, K., Woods, M., Pitt, D., Nesbitt, D., & Etheridge, D. (2012). LiDAR sampling density for forest resource inventories in Ontario, Canada. Remote Sensing, 4 (4), 830-848 Van Aardt, J.A.N., Wynne, R.H., & Oderwald, R.G. (2006). Forest volume and biomass estimation using small-footprint LiDAR-distributional parameters on a per-segment basis. Forest Science, 52 (6), 636-649 van Leeuwen, M., & Nieuwenhuis, M. (2010). Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research, 129 (4), 749-770 Vastaranta, M., Kankare, V., Holopainen, M., Yu, X., Hyyppä, J., & Hyyppä, H. (2012). Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data. ISPRS Journal of Photogrammetry and Remote Sensing, 67 (1), 73-79 Vauhkonen, J., Packalen, P., Malinen, J., Pitkänen, J., & Maltamo, M. (2014). Airborne laser scanning-based decision support for wood procurement planning. Scandinavian Journal of Forest Research, 29, 132-143 Vauhkonen, J., Ene, L., Gupta, S., Heinzel, J., Holmgren, J., Pitkänen, J., Solberg, S., Wang, Y., Weinacker, H., Hauglin, K.M., Lien, V., Packalén, P., Gobakken, T., Koch, B., Næsset, E., Tokola, T., & Maltamo, M. (2012). Comparative testing of single-tree detection algorithms under different types of forest. Forestry, 85 (1), 27-40

21

Introduction

Villikka, M., Packalén, P., & Maltamo, M. (2012). The suitability of leaf-off airborne laser scanning data in an area-based forest inventory of coniferous and deciduous trees. Silva Fennica, 46 (1), 99-110 Wagner, W., Ullrich, A., Ducic, V., Melzer, T., & Studnicka, N. (2006). Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 60 (2), 100-112 White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N.C., Cook, B.D., Pitt, D., & Woods, M. (2013). A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. Information Report FI-X-010, Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, BC Wing, B.M., Ritchie, M.W., Boston, K., Cohen, W.B., Gitelman, A., & Olsen, M.J. (2012). Prediction of understory vegetation cover with airborne LiDAR in an interior ponderosa pine forest. Remote Sensing of Environment, 124, 730-741 Wulder, M.A., Coops, N.C., Hudak, A.T., Morsdorf, F., Nelson, R., Newnham, G., & Vastaranta, M. (2013). Status and prospects for LiDAR remote sensing of forested ecosystems. Canadian Journal of Remote Sensing, 39, S1-S5 Wulder, M.A., White, J.C., Nelson, R.F., Næsset, E., Ørka, H.O., Coops, N.C., Hilker, T., Bater, C.W., & Gobakken, T. (2012). LiDAR sampling for large-area forest characterization: A review. Remote Sensing of Environment, 121, 196-209 Wulder, M.A., Bater, C.W., Coops, N.C., Hilker, T., & White, J.C. (2008). The role of LiDAR in sustainable forest management. Forestry Chronicle, 84 (6), 807-826 Xue, B.-L., Kumagai, T., Iida, S., Nakai, T., Matsumoto, K., Komatsu, H., Otsuki, K., & Ohta, T. (2011). Influences of canopy structure and physiological traits on flux partitioning between understory and overstory in an eastern Siberian boreal larch forest. Ecological Modelling, 222 (8), 1479-1490 Yang, R., & Friedl, M.A. (2003). Modelling the effects of three-dimensional vegetation structure on surface radiation and energy balance in boreal forests. Journal of Geophysical Research D: Atmospheres, 108 (16), GCP10-1 - GCP10-11 Zhao, K., Popescu, S., Meng, X., Pang, Y., & Agca, M. (2011). Characterizing forest canopy structure with LiDAR composite metrics and machine learning. Remote Sensing of Environment, 115 (8), 1978-1996 Zhao, K., Popescu, S., & Nelson, R. (2009). LiDAR remote sensing of forest biomass: A scaleinvariant estimation approach using airborne lasers. Remote Sensing of Environment, 113 (1), 182-196 Zimble, D.A., Evans, D.L., Carlson, G.C., Parker, R.C., Grado, S.C., & Gerard, P.D. (2003). Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sensing of Environment, 87 (2-3), 171-182

22

Chapter

2 Flugzeuggestütztes Laserscanning für ein operationelles Waldstrukturmonitoring Leiterer, R., Muecke, W., Morsdorf, F., Hollaus, M., Pfeifer, N. and Schaepman, M.E.

Published in Photogrammetrie, Fernerkundung, Geoinformation, 2013 (3), 173-184

23

Chapter 2

Abstract

Operational forest structure monitoring using airborne laser scanning. The structure of forests influences the global biochemical cycles and can serve as an indicator to estimate the conservation potential for biodiversity and to determine forest stand resistance to disturbances. Airborne laser scanning (ALS) systems have been proven as suitable tools to provide horizontal as well as explicit vertical information due to the canopy penetration of the emitted signal. We developed robust methods based on multi-temporal ALS data to provide a more efficient monitoring of forest structure variables. The derived forest structure information includes: i) the individual tree delineation, ii) the detection and description of understory and ground cover, and iii) the detection of dead wood. We used full-waveform ALS data under foliated and defoliated conditions in dense, deciduous dominated forest stands in the Lägern (Switzerland) and the Uckermark (Germany). Based on the ALS point cloud with the traditional geometrical characteristics and the related full-waveform information, we applied the following methods: i) hierarchical, 3D-clustering and alpha shape derivation for the individual tree delineation, ii) grid based, vertical stratification for understory detection, and iii) combination of map algebra and vectorization for the dead wood analysis. The validation showed high accuracies for the derived forest structure information following the requirements of traditional forest inventories. We conclude that it is possible to detect and characterize the forest structure with robust methods based on full-waveform ALS data; however, the availability of foliated/ defoliated ALS data with a high point density is indispensable.

Data collection was performed by R. Leiterer and W. Muecke, study design was developed by R. Leiterer, W. Muecke, F. Morsdorf and M. Hollaus, data analysis was performed by R. Leiterer, W. Muecke and F. Morsdorf, manuscript was written by R. Leiterer, W. Muecke, F. Morsdorf, M. Hollaus, N. Pfeifer and M.E. Schaepman.

24

Chapter 2

Zusammenfassung

Die Struktur des Waldes hat einen signifikanten Einfluss auf die globalen biogeochemischen Stoffkreisläufe und kann darüber hinaus als Indikator dienen, um das Potential zum Erhalt der Biodiversität abzuschätzen und die Widerstandsfähigkeit des Waldes gegen äußere Einflüsse zu bestimmen. Flugzeuggestütztes Laserscanning (ALS) bietet hierbei die Möglichkeit einer räumlich hochaufgelösten Erfassung und Beschreibung sowohl der horizontalen als auch der vertikalen Waldstruktur. Wir stellen robuste Verfahren basierend auf flugzeuggestützten Laserscanningdaten vor, um eine Extraktion von forstwirtschaftlich und -wissenschaftlich relevanten Strukturinformationen zu ermöglichen. Dies beinhaltet: i) die Einzelbaumextraktion, ii) die

Bestimmung

von

Unterwuchs

und

Bodenbedeckung

und

iii)

die

Totholzerkennung. Die Datengrundlage bestand aus multi-temporalen, full-waveform Laserdaten in dichtem Laub- und Mischwald für Testgebiete in der Schweiz (Lägern) und in Deutschland (Uckermark). Basierend auf der ALS-Punktwolke mit ihren geometrischen Attributen und den zugehörigen full-waveform Eigenschaften wurden folgende Methoden angewendet: i) hierarchisches, 3D-Clustering und die Ableitung von

alpha

shapes

für

die

Einzelbaumextraktion,

ii)

rasterbasierte,

vertikale

Stratifizierung für die Charakterisierung von Unterwuchs, und iii) die Kombination aus map algebra und Vektorisierung für die Totholzanalyse. Die erzielten Genauigkeiten der abgeleiteten Strukturvariablen entsprachen den Anforderungen der traditionellen Forstinventur. Vorbehaltlich der Verfügbarkeit einer entsprechenden Datengrundlage (multi-temporale ALS-Daten mit hohen Punktdichten) ist es mit den vorgestellten robusten

Methoden

möglich,

ein

großflächiges

und

operationelles

Waldstrukturmonitoring durchzuführen.

25

Chapter

3 Towards automated characterization of canopy layering in mixed temperate forests using airborne laser scanning Leiterer, R., Torabzadeh, H., Furrer, R., Schaepman, M.E. and Morsdorf, F.

Published in Forests, 2015 (6), 4146-4167 43

Chapter 3

Abstract

Canopy layers form essential structural components, affecting stand productivity and wildlife habitats. Airborne laser scanning (ALS) provides horizontal and vertical information on canopy structure simultaneously. Existing approaches to assess canopy layering often require prior information about stand characteristics or rely on predefined height thresholds. We developed a multi-scale method using ALS data with point densities >10 pts/m2 to determine the number and vertical extent of canopy layers (canopylayer, canopylength), seasonal variations in the topmost canopy layer (canopytype), as well as small-scale heterogeneities in the canopy (canopyheterogeneity). We first tested and developed the method on a small forest patch (800 ha) and afterwards tested transferability and robustness of the method on a larger patch (180,000 ha). We validated the approach using an extensive set of ground data, achieving overall accuracies >77% for canopytype and canopyheterogeneity, and >62% for canopylayer and canopylength. We conclude that our method provides a robust characterization of canopy layering supporting automated canopy structure monitoring.

Data collection was performed by R. Leiterer, study design was developed by R. Leiterer, F. Morsdorf and H. Torabzadeh, data analysis was performed by R. Leiterer and H. Torabzadeh, manuscript was written by R. Leiterer, F. Morsdorf, R. Furrer and M.E. Schaepman.

44

Chapter

4 Forest canopy-structure characterization: A data-driven approach Leiterer, R., Furrer, R., Schaepman, M.E. and Morsdorf, F.

Published in Forest Ecology and Management, 2015 (358), 48-61

73

Chapter 4

Abstract

Forest canopy structure influences and partitions the energy fluxes between the atmosphere and vegetation. It serves as an indicator of a variety of biophysical variables and ecosystem goods and services. Airborne laser scanning (ALS) can simultaneously provide horizontal and vertical information on canopy structure. Existing approaches to assess canopy structure often focus on in situ collected structural variables and require a substantial set of prior information about stand characteristics. They also rely on pre-defined spatial units and are usually dependent on site-specific model calibrations. We propose a method to provide quantitative canopy-structure descriptors on different scales, retrieved from ALS data. The approach includes (i) a sensitivity assessment and a quantification of ALS-derived canopy-structure information dependent on ALS data properties, (ii) an automatic determination of the most feasible spatial unit for canopy-structure characterization, and (iii) the derivation of canopy-structure types (CSTs) using a hierarchical, multiscale classification approach based on Bayesian robust mixture models (BRMM), satisfying structurally homogenous criteria without the use of in situ calibration information. The CSTs resulted in retrievals of canopy layering (single-, two-, and multi-layered canopies) and canopy types (deciduous or evergreen canopies). Retrievals classified seven CSTs with accuracies ranging from 52% to 82% user accuracy (canopy layering) and 89-99% user accuracy (canopy type). The method supports a data-driven approach, allowing for an efficient monitoring of canopy structure.

Data collection was performed by R. Leiterer, study design was developed by R. Leiterer, F. Morsdorf, H. R. Furrer and M.E. Schaepman, data analysis was performed by R. Leiterer, F. Morsdorf and R. Furrer, manuscript was written by R. Leiterer, F. Morsdorf, R. Furrer and M.E. Schaepman.

74

Chapter

5 Synopsis

113

Synthesis

5.1 Main results The main results of this dissertation are structured according to the main research questions presented in chapter 1.3.1 and the respective publications (chapters 2–4). 5.1.1

How can we extract information about the canopy structure using ALS as comprehensively as possible while being physically accurate by combining ITD/ITC and ABA?

In chapter 2, a series of findings are reported, discussing possibilities for a more robust application of ALS-based canopy structure characterization. This includes a physicallyoriented derivation of canopy structure variables such as canopy surface area or canopy volume, which so far have been derived in traditional forest inventory only indirectly based on allometric equations. The presented approach combines improved individual tree detection and crown delineation with an area-based assessment of forest understory and deadwood. The advantage of combining ITD/ITC and ABA methods was already pointed out by Yu et al. (2010), Vastaranta et al. (2012) and Kankare et al. (2013) but had not yet been applied in such a comprehensive matter. In particular, the result of the assessment of understory and deadwood outperforms previous studies, which either used a less detailed and spatially less explicit description of understory (e.g. Swetnam et al. 2014, Wing et al. 2012, Vehmas et al. 2011) and deadwood (e.g. Russel et al. 2015, Pesonen et al. 2008), or did not achieve comparably high accuracies. The characterization of canopy structure shows a high level of consistency with common forest inventory measurements and meets the requirements of European forest inventory practices in terms of automation, accuracy, spatial resolution and information content (cf. Ferretti & König 2013). Therefore, the applied canopy structure characterization using ALS has the potential to improve and support the regular monitoring of the forest canopy structure. Moreover, we demonstrate the potential of derived canopy structure features (e.g. understory characterization or individual tree/tree crown dimensions) for parameterization of 3D radiative transfer models, enabling a physically based simulation of Earth observation data by forward simulation from the leaf to the sensor level (Schneider et al. 2014). Considering that Kötz et al. (2004) pointed out that information about the canopy structure is the major

114

Chapter 5

source of uncertainty when characterizing canopy reflectance by radiative transfer models, our canopy structure characterization offers a good supplement to the radiometric information. Such a comprehensive characterization of canopy structure can also be used to calibrate and/or validate future satellite missions such as the Global Ecosystem Dynamics Investigation (GEDI) LiDAR, aimed to systematically probe the depths of the forest from space (Dubayah et al. 2014). There are, however, some limitations to the proposed ALS-based canopy structure characterization. An important point that must be considered is whether sensor type and configuration (e.g. flight height or scan angle range) have an influence on the proposed canopy structure characterization (cf. Lim et al. 2008, Hopkinson 2007, Chasmer et al. 2006). We address this point in more detail in chapter 5.1.3. The transferability of the method to forests with substantially different canopy structures needs to be explored. In forests with a highly complex canopy structure such as in the tropics, for example, it is very likely that dense canopy layers hinder a full sampling of the canopy structure and, therefore, a characterization of the lower canopy parts such as understory or lying deadwood, may not be possible. Possible occlusion effects, however, can be quantified using ray-tracing approaches such as the ones applied by Kükenbrink et al. (2015). Some features of the canopy structure cannot be accessed directly, but are very important for forest management or forest ecology. For example, the effects of browsing by herbivores on tree regeneration can strongly alter successional pathways in forests (cf. Hjeljord et al. 2014, Akashi 2009), but cannot be detected directly using ALS. The proposed canopy structure characterization, however, could be used as model input to estimate quantity and quality of herbaceous forage as an important indicator for foraging behaviour of herbivores (Ewald et al. 2014). Another example is the quantification of forest biomass, which is also not directly possible using ALS. Nevertheless, numerous studies reported that, if canopy structure information as derived by us, is used as auxiliary information, the precision of biomass estimates is greatly increased (e.g. McRoberts et al. 2015, Kankare et al. 2015, Skowronski et al. 2014, Næsset & Gobakken 2008). In summary, our approach works towards an accurate, comprehensive and spatially explicit characterization of canopy structure. The semi-automatically derived canopy structure variables are well suited for a wide range of applications, including forest inventory, forest ecology and the analysis of radiative transfer in forest canopies.

115

Synthesis

5.1.2

How can we use ALS data to characterise canopy layering and foliage distribution on a regional scale in an automatic way and with as little as possible use of ancillary information?

In chapter 2, we presented an area-based, multi-scale method to derive accurate information about canopy layering and foliage distribution on a regional scale, whereby the definition of ‘canopy layering’ and underlying spatial units were given by the forestry department of the Canton of Aargau, Switzerland. The method was developed to work in a semi-automatic manner, applicable to ALS data with different properties (within certain limits) and controllable by parameters according to stakeholders needs. We tested the transferability by developing the method on a small forest patch (800 ha) using ALS data with very high point densities and applied it afterwards to a larger patch (180 000 ha) using ALS data with ‘standard’ point densities. As a result of using the multi-scale method, we were able to map small-scale variations in canopy layering and foliage distribution, which occured, to our knowledge, for the first time in this way. Information about small-scale variations in the canopy is important for assessing biodiversity and ecosystem habitats and their detection and monitoring can contribute to sustainable forest management (cf. Gao et al. 2014). The use of multi-seasonal ALS data enables an accurate distinction between evergreen and deciduous canopies with overall accuracies comparable to the highest obtained accuracies in existing studies (cf. Torabzadeh et al. 2015a, Lindberg et al. 2014, Kim et al. 2009). Our method does not require prior information about stand characteristics or relies on pre-defined height thresholds for an existing stratification of the canopy, which represents a major improvement in comparison to previous studies addressing canopy layering (cf. Tang et al. 2014, Whitehurst et al. 2013, Ferraz et al. 2012, Wang et al. 2008). It needs, however, an exact definition of ‘canopy layering’ that should be a compromise between the information contained in the ALS data and the definition used in the stakeholders’ field of interest, which in our case is forest inventory (cf. Tiede et al. 2004, Moffett 2000, Parker 2000). We noticed that the compromise we found with regard to definition and assessment of canopy layering was not suitable for the practitioners in the field. Canopy layering used in traditional forest inventory, for example, is more related to the composition of tree development

116

Chapter 5

stages and less focused on actual foliage distribution. For the ALS-based assessment of canopy layering it is difficult to semantically attribute vegetation echoes from the lower canopy parts, because these echoes can either come from the forest floor, from young forest or they can be caused by low branches of larger trees. These semantic classes are, however, mandatory for addressing canopy layering from a forest inventory point of view. A combination with methods at the individual tree level could help to address this issue, which would, for example, allow partial distinguishing of understory shrubs/herbs from understory trees (Torabzadeh et al. 2015b, Ferraz et al 2012, Wing et al. 2012). In summary, automated and transferable characterization of canopy structure in terms of canopy layering and foliage distribution using ALS is feasible (also on regional scales), but the harmonization with existing approaches to assess canopy layering, in particular the ones used in forest inventory, remains challenging. For applications on larger scales, the use of auxiliary data such as land use/land cover maps or forest stand polygons could further improve canopy structure classifications, by, for example, excluding areas which are not of interest or using land-cover–specific parameterizations of the algorithm. 5.1.3

How and to what extent can we describe the continuous canopy structure using the data-driven, multi-scale concept of canopy structure types, considering the properties of ALS data?

In chapter 4 we presented a data-driven, multi-scale approach for the characterization of the canopy structure. Data-driven means that, in contrast to the work presented in chapters 2 and 3, the characterization of canopy structure was done independently of user requirements and possible application fields. The approach includes (i) the selfcontained determination of the most suitable spatial scales for the canopy structure characterization, depending on actual canopy structure characteristics and ALS data properties, and (ii) the classification of the continuous canopy structure into discrete classes (so-called canopy structure types, CSTs) using a hierarchical, multi-scale classification approach based on Bayesian robust mixture models (BRMMs). The sensitivity analysis of ALS data properties (scan angle, point density and acquisition date) addresses two important topics that are subject of controversial

117

Synthesis

discussions. First, we found that the added value of leaf-off data on the relative frequency distribution of echoes within a given grid cell is relatively low if leaf-on data are available. This finding largely depends on the combination of grid-cell size and respective point density. Moreover, site specific effects cannot entirely be ruled out. It is important to stress this, because in contrast to our findings, Næsset (2005), Ørka et al. (2010) and Villikka et al. (2012) reported an added value of leaf-off acquisitions over leaf-on acquisitions, but the ALS data used in these studies had much lower point densities and the direct comparability with their results is therefore limited. Second, we noticed that with increasing grid-cell size the relevance of point density for canopy structure characterization decreases. This is in accordance to the findings by Hawbaker et al. (2010) and Treitz et al. (2012), who pointed out the low influence of point density when working on larger spatial units. We showed, however, that differences in the lower point density ranges (e.g., 1–5 pts/m2) have a strong effect on the canopy structure characterization; whereas point densities of more than 10 pts/m2 only slightly improve canopy structure characterization, irrespective of the grid cell size. Consequently, analyses regarding ALS data properties should not only consider different sensor specifications (cf. Lim et al. 2008, Hopkinson 2007, Chasmer et al. 2006), but should also be performed taking into consideration individual target variables. Variations in scan angles did not influence the canopy structure characterization in our analysis; however, previous studies have found that the effect of the scan angle on ALS-based vegetation metrics strongly increases for scan-angles larger than ±15° (Montaghi 2013, Disney et al. 2010). Moreover, scan angle effects should to be considered in highly fragmented forests or in forests with a significantly higher gap fraction (cf. Holmgren et al. 2003, Ahokas et al. 2005) as well as in the use of full-waveform features for the estimation of canopy structure variables (Kim et al. 2009). The data-driven determination of the most suitable grid cell size for canopy structure characterization considers the characteristics of the canopy structure and does not require any previous knowledge about the forest. This also means, however, that changes of the canopy structure can influence the definition of grid cell size, even if similar ALS data are available. Consequently, the self-contained determination of the most suitable spatial scales for canopy structure characterization needs to be performed anew for each new study site. In cases of sparse canopies, the suitability of

118

Chapter 5

data-driven determination of grid cell sizes remains disputable, as the resulting grid cell sizes must be large enough to cover and describe the openness of the forest. What also needs to be explored is the transferability of the presented method to substantially different canopy structures: if the concept of CSTs is applied to large areas with very different forest types, the distinctness of CSTs within a specific forest type becomes increasingly smaller and a spatially stratified clustering is recommended. In summary, the developed data-driven approach enables a comprehensive and robust characterization of the continuous structure of forest canopies, providing a traceable base for further canopy structure analysis. Moreover, the use of BRMM renders the CST derivation robust and is in line with current efforts to harmonize forest inventories and forest condition monitoring (Gasparini et al. 2013, Ferretti 2013).

5.2 Synthesis In chapter 2 and 3 we showed, how advanced ITD/ITC and area-based approaches as well as a combination of both can improve the characterization of canopy structure. The most important aspects of the improvements in comparison to existing approaches were i) the increase of performance by developing mainly automatic methods, ii) the more objective and transparent framework, and iii) the additional information which have been gained, such as the assessment of forest understory, forest layering or deadwood. These improved approaches, however, were still focused on canopy structure variables defined by the specific application and/or user requirements and needed prior information about stand characteristics. Moreover, the spatial units at which canopy structure was described were defined to coincide with the field data and the methods were dependent on the selected canopy-structure variables. Inherent spatial scales of the investigated structural components were thus not taken into account. Considering these limitations, we developed the data-driven concept of CSTs, following objective criteria, working less scale-dependent and taking into account the specific ALS data properties. The proposed designation of canopy structure classes using CSTs is based only on the ALS data and the actual canopy structure and thus does not necessarily correspond to existing groupings and/or classifications of canopy structure. This also means, that the use of CSTs neither provides information at the

119

Synthesis

individual tree level nor matches to canopy structure variables commonly measured in the field or estimated using area-based approaches. Consequently, as the CST approach

enables

canopy

structure

characterization

beyond

usual

in

situ

measurements, the absolute need for field data to calibrate LiDAR measurements as stated by, e.g. Wulder et al. (2010) must be questioned. Which not means, that we want to judge if the CST approach is better or worse in comparison to existing approaches. CSTs should be therefore considered as a new conceptualisation of how canopy structure can be characterised in a quantitative and fully-automatic way. This new conceptualization, however, has many implications which is discussed as follows. In a first step, we will address the scientific implications. The data-driven CST approach is, as far as we know, the first method enabling transparent, consistent and automatic wall-to-wall quantification of canopy structure, detached from requirements and constraints given by users. This is an important issue, in particular in research fields where no strict grouping/classification schemes exist but a discretization of the continuous entity canopy structure is nevertheless necessary. The high level of freedom, however, implies a complex procedure of interpretation and harmonization of derived CSTs. One of the most critical points in this context is semantic clarification. The CSTs, for example, are grouped according to similarity of ALS based canopy profiles. But what is, in this context, the meaning of similarity? Is a similarity in the maximum canopy height more, less or equally important for the distinction of CSTs than the similarity in vertical foliage distribution? In the presented approach, the specific characteristics of the canopy profiles are not or equally weighted, which leads do that the derived CSTs do not meet the semantic specifications for most of the present applications fields of ALS based canopy structure characterization. To bridge the existing semantic gap (cf. Ciocca et al. 2012, Liu et al. 2007) between the richness of human semantics related to canopy structure on the one side and the structure-related features contained in the CSTs on the other side was therefore one of the most challenging issues for the evaluation of the CST approach. We addressed this issue by including prosemantic features for the forestry experts’ interpretation of the CSTs, resulting in high agreements between interpreted CST characteristics and structural variables, commonly used in forest inventory. This contradicts, however, the claim for independency of user’s requirements. On the basis of this knowledge, we see therefore the absolute necessity to not only consider components related to data in the

120

Chapter 5

development process of data-driven approaches but also investigate suitable semantic concepts, meeting as good as possible the requirements of an objective framework. The issue of the semantic gap is closely related to implications related to the application of CSTs. In general, we have some reservations regarding the wide-spread usage of this kind of data-driven approach in the near future, since practitioners not only need to adopt their existing (operational) workflows of canopy structure characterization but also need to rethink semantic meanings of structural variables/groups. This raises the questions how many structural groups can be generally semantically distinguished, i.e. what is the dimensionality of canopy structure, and whether the maximum possible number of CSTs for an area of interest should be limited for a better applicability. The answer, of course, is mainly driven by the specific application field/user requirements and can not be generalized. Another challenge is the application of CSTs within a monitoring framework. In such cases, CSTs need to be comparable but because of possible differences in spatial scales, semantics and amounts of resulting CSTs it is necessary to develop and apply complex statistical and semantical harmonisation procedures afterwards (Kosmidou et al. 2014, Verburg et al. 2011). Finally, the common approach in forestry to evaluate the practical applicability of a new approach with field measurements is difficult to implement. Although the validation approach in our study was designed in cooperation with the stakeholder, the assessment of CSTs in the field by forestry experts turned out to be highly subjective and elusive, confirming the findings of Foody (2010), Haara and Leskinen (2009) and McElhinny et al. (2005). Moreover, as already pointed out before, differences in semantics and definitions regarding the structure of forest canopies (cf. Sorites Paradox, Cargile (1969)) and differences in the spatial scales used to derive canopy structure variables in the field made the evaluation in many cases difficult or impossible. These difficulties and limitations may explain the limited number of studies in this field of research (cf. Vauhkonen & Imponen 2015, Kane et al. 2010). Therefore, although the CST evaluation as we applied indicated a high application potential of the presented approach, we see the evaluation of the CSTs rather in areas without rigid assessment rules for structure characterization, for example in forest ecology (Dial et al. 2004). Nevertheless, the additional information CSTs can provide and the objective frame of the CST determination, created an additional demand for their implementation. It remains to be

121

Synthesis

seen if the advantages of a data-driven approach compensate for its limitations. The most promising perspective in terms of application, nevertheless, is the combination of a data-driven, area-based approach, such as presented in chapter 4, with ITD/ITC approaches following objective criteria, such as presented in chapter 2. This is, in our view, the only way to take into account, that most canopy structure components have inherent spatial scales, and the choice of the respective level and the related spatial unit should comply with the investigated structural component. The lessons and experiences emerging from the performed investigations in this thesis show, that the relationship between data-driven concepts and possible fields of application must be critically questioned. As soon as possible applications are considered in the development of data-driven approaches, it con not be claimed as free of constraints anymore. Moreover, as soon as LiDAR metrics are converted to structural metrics/variables, the complex issue of the semantic gap must certainly be taken into account. In current research, these aspects are in fact not considered sufficiently.

5.3 Conclusion and Outlook As one of the most biologically diverse terrestrial ecosystems on Earth, forests play a pivotal role for global biogeochemical and biophysical cycles and provide a wide range of valuable ecosystem goods and services (Pan et al. 2013, Bonan 2008). Earth observation data are an important source for obtaining quantitative descriptions of the current state of forests as well as for predicting forthcoming changes. A particularly crucial constituent of forest ecosystems’ functioning is the structure of forest canopies. The availability of ALS systems, which are among the most recent Earth observation systems, has greatly improved the ability to characterise the forest canopy structure over large areas in not only the horizontal but also the vertical dimension (Kane et al. 2010, Næsset 2004). In view of current projects based on terrestrial (Kankare et al. 2015, Ashcroft et al. 2014, Kelbe et al. 2014) and satellite laser scanning (Dubayah et al. 2014), ALS will play an important role in linking structure information from the individual plant to canopy structure characterization on a global scale, highlighting the need for comparable methods of canopy structure characterization on multiple scales within an objective

122

Chapter 5

framework. We showed in this thesis the potential of ALS for an accurate characterization of forest canopy structure in mixed, temperate forests with as little as possible use of ancillary information. Using improved and newly developed methods, a series of structural variables were derived and investigated with regard to their suitability for applications in forest inventory and management, forest ecology and radiative transfer modelling. Table 5.1 gives an overview of the main structural variables and methodological approaches that were addressed in this thesis with reference to the related publications completed in the framework of this thesis. The resulting accuracies for canopy structure variables for which an accuracy assessment was possible, largely meet with stakeholders’ expectations and are of similar or higher magnitudes than reported in existing studies.

Table 5.1 Canopy structure variables addressed in this thesis, the applied methods (ITD/ITC = individual tree/tree crown level, ABA = area-based approaches), resulting variable accuracies (OA = Overall Accuracy, r2 = coefficient of determination) and related publications. Canopy structure variables

Method (Variable accuracy)

Tree position

ITD/ITC (± 1.55 - 1.74 m)

Tree/ canopy height

ITD (± 0.38 - 0.87 m)

Tree crown dimensions

ITC (± 2.8 m)

Publication (Torabzadeh et al. 2015b, Leiterer et al. 2013) (Torabzadeh et al. 2015b, Leiterer et al. 2013) (Leiterer et al. 2013)

Tree/ canopy type (deciduous vs. evergreen)

ITC/ABA (OA = 86.4 - 96.2%)

(Leiterer et al. 2015a, Leiterer et al. 2015b, Torabzadeh et al. 2015a)

Canopy length/ foliage distribution

ITC/ABA (OA = 61.9 - 70.3%)

(Leiterer et al. 2015a, Leiterer et al. 2015b, Schneider et al. 2014)

ABA (r2 = 0.78)

(Leiterer et al. 2013)

Canopy layering

ABA (OA = 59.3 - 69.2 %)

(Leiterer et al. 2015a)

Understory/ understory trees

ITD/ITC/ABA (OA = 64.19%)

Canopy cover

Small-scale variations in canopy structure

ABA (OA = 77.7 to 81.5%)

(Torabzadeh et al. 2015b, Leiterer et al. 2013) (Leiterer et al. 2015a, Leiterer et al. 2015b)

123

Synthesis

The designed data-driven concept of canopy structure types (CSTs) detaches itself from the usual approach of considering ALS only as a tool for regionalizing field measurements to support forest inventory and management practices. The use of CSTs is a step towards a comparable and more comprehensive canopy structure characterization, independent of restrictions imposed by stakeholders/users and specific application fields, but nevertheless providing a valuable base for further analysis extending to all fields of forestry applications. Once implemented, CSTs can serve as a proxy for ecosystem service-based assessments in forests, which includes the long-term goal of monitoring forest change with high precision and accuracy. Nevertheless, some limitations still need to be considered: the validation shows that the determination of canopy structure variables at the individual tree level can only be carried out with limitations due to specific stand characteristics and requires leafon/leaf-off ALS data with high point density. This also applies to the assessment of occurrence and height of understory vegetation, which strongly restricts the large-scale usage of the developed methods at this time. Thus, an application of the developed method using ALS data with low point densities can be recommended only with restrictions and by accepting increasing uncertainties in terms of canopy structure characterization. What remains an open question is the transferability and broad applicability of the CST concept, i.e. the application of the CST concept to a variety of open to dense forests, to different forest types and to larger scales. We also still need to investigate the relationship of CSTs with different established forest ecosystem goods and services. Moreover, we have not yet cross-compared CSTs based on very different ALS data, i.e. we have not dealt with the problem of finding suitable harmonization methods for CSTs with varying amount of structure types and different cell sizes. Further research is on-going in two areas: first, the understory classification presented in chapter 2 will be extended to a larger area and will contain information about areas, that were not fully sampled by the ALS measurements. In addition, the link between the function of understory as a hiding cover and food source for animals and the actual movement patterns of animals will be investigated. The aim of this analysis is to support ecosystem research, in which understory plays a major role, as it makes up the largest percentage of vascular plant diversity in a given forest ecosystem, comprises the habitat for a wide variety of animals, limits the recruitment of tree species and influences ecosystems functions such as carbon and nitrogen cycling (e.g.

124

Chapter 5

Wing et al. 2012, Kayes and Tinker 2012, Graf et al. 2009, Chappell 2006). Second, we will investigate in more detail the relationship between selected LiDAR metrics that describe the canopy structure and the spatial unit, at which these metrics were derived (cf. chapter 4). Moreover, we will investigate how this relationship will change depending on ALS data properties. The aim of this analysis is to receive functions for each of the selected LiDAR metrics, describing the dependency of the LiDAR metrics and their respective predictor variables. These results will address and emphasize representation problems which can occur in cases where LiDAR metrics are derived based on spatial units given by the field/reference measurements or stakeholders’ requirements.

125

Synthesis

5.4 References Ahokas, E., Yu, X., Oksanen, J., Kaartinen, H., & Model, D.T. (2005). Optimization of the scanning angle for countrywide laser scanning. ISPRS WG III/3, III/4, V/3 Workshop “Laser scanning 2005”, Enschede, the Netherlands, September 12-14, 2005, 115-119 Akashi, N. (2009). Browsing damage by sika deer on trees in young plantations and its relation to relative deer density indices in Hokkaido, Japan. Nihon Ringakkai Shi/Journal of the Japanese Forestry Society, 91 (3), 178-183 Ashcroft, M.B., Gollan, J.R., & Ramp, D. (2014). Creating vegetation density profiles for a diverse range of ecological habitats using terrestrial laser scanning. Methods in Ecology and Evolution, 5 (3), 263-272 Bonan, G.B. (2008). Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320 (5882), 1444-1449 Cargile, J. (1969). The Sorites Paradox. British Journal for the Philosophy of Science, 20 (3), 193202 Ciocca, G., Cusano, C., Santini, S., & Schettini, R. (2011). Halfway through the semantic gap: Prosemantic features for image retrieval. Information Sciences, 181 (22), 4943-4958 Dial, R., Bloodworh, B., Lee, A., Boyne, P., & Heys, J. (2004). The distribution of free space and its relation to canopy composition at six forest sites. Forest Science, 50, 312-325 Disney, M.I., Kalogirou, V., Lewis, P., Prieto-Blanco, A., Hancock, S., & Pfeifer, M. (2010). Simulating the impact of discrete-return LiDAR system and survey characteristics over young conifer and broadleaf forests. Remote Sensing of Environment, 114 (7), 1546-1560 Dubayah, R., Goetz, S.J., Blair, J.B., Fatoyinbo, T.E., Hansen, M., Healey, S.P., Hofton, M.A., Hurtt, G.C., Kellner, J., Luthcke, S.B., & Swatantran, A. (2014). The Global Ecosystem Dynamics Investigation. Proceedings, AGU, San Francisco, USA, 15-19 December, 1 p. Ewald, J., Braun, L., Zeppenfeld, T., Jehl, H., & Heurich, M. (2014). Estimating the distribution of forage mass for ungulates from vegetation plots in Bavarian forest national park. Tuexenia, 34 (1), 53-70 Ferraz, A., Bretar, F., Jacquemoud, S., Gonçalves, G., Pereira, L., Tomé, M., & Soares, P. (2012). 3-D mapping of a multi-layered Mediterranean forest using ALS data. Remote Sensing of Environment, 121, 210-223 Ferretti, M., & König, N. (2013). Chapter 20 - Quality Assurance in International Forest Monitoring in Europe. In: Marco Ferretti and Richard Fischer, Editor(s), Developments in Environmental Science, Elsevier, 2013, 12, 387-396 Foody, G.M. (2010). Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sensing of Environment, 114 (10), 2271-2285

126

Chapter 5

Gao, T., Hedblom, M., Emilsson, T., & Nielsen, A.B. (2014). The role of forest stand structure as biodiversity indicator. Forest Ecology and Management, 330, 82-93 Gasparini, P., Di Cosmo, L., Cenni, E., Pompei, E., & Ferretti, M. (2013). Towards the harmonization between national forest inventory and forest condition monitoring. Consistency of plot allocation and effect of tree selection methods on sample statistics in Italy. Environmental Monitoring and Assessment, 185 (7), 6155-6171 Chappell, C.B. (2006). Upland plant associations of the Puget Trough ecoregion, Washington. Natural Heritage Rep. 2006-01. Washington Department of Natural Resources, Natural Heritage Program, Olympia, Washington Chasmer, L., Hopkinson, C., Smith, B., & Treitz, P. (2006). Examining the influence of changing laser pulse repetition frequencies on conifer forest canopy returns. Photogrammetric Engineering and Remote Sensing, 72 (12), 1359-1367 Graf, R., Mathys, L., & Bollmann, K. (2009). Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps. Forest Ecology and Management, 257 (1), 160-167 Haara, A., & Leskinen, P. (2009). The assessment of the uncertainty of updated stand-level inventory data. Silva Fennica, 43 (1), 87-112 Hawbaker, T.J., Gobakken, T., Lesak, A., Trømborg, E., Contrucci, K., & Radeloff, V. (2010). Light detection and ranging-based measures of mixed hardwood forest structure, Forest Science, 56 (3), 313-326 Hjeljord, O., Histøl, T., & Wam, H.K. (2014). Forest pasturing of livestock in Norway: effects on spruce regeneration. Journal of Forestry Research, 25 (4), 941-945 Holmgren, J., Nilsson, M., & Olsson, H. (2003). Simulating the effects of LiDAR scanning angle for estimation of mean tree height and canopy closure. Canadian Journal of Remote Sensing, 29 (5), 623-632 Hopkinson, C. (2007). The influence of flying altitude, beam divergence, and pulse repetition frequency on laser pulse return intensity and canopy frequency distribution. Canadian Journal of Remote Sensing, 33 (1-4), 312-324 Kane, R., Bakker, J.D., McGaughey, R.J., Lutz, J.A., Gersonde, R.F., & Franklin, J.F. (2010). Examining conifer canopy structural complexity across forest ages and elevations with LiDAR data. Canadian Journal of Forest Research, 40 (4), 774-787 Kankare, V., Vauhkonen, J., Holopainen, M., Vastaranta, M., Hyyppä, J., Hyyppä, H., & Alho, P. (2015). Sparse density, leaf-off airborne laser scanning data in aboveground biomass component prediction. Forests, 6 (6), 1839-1857 Kankare, V., Liang, X., Vastaranta, M., Yu, X., Holopainen, M., & Hyyppä, J. (2015). Diameter distribution estimation with laser scanning based multisource single tree inventory. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 161-171

127

Synthesis

Kankare, V., Vastaranta, M., Holopainen, M., Räty, M., Yu, X., Hyyppä, J., Hyyppä, H., Alho, P., & Viitala, R. (2013). Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR. Remote Sensing, 5 (5), 2257-2274 Kayes, L.J., & Tinker, D.B. (2012). Forest structure and regeneration following a mountain pine beetle epidemic in southeastern Wyoming. Forest Ecology and Management, 263, 57-66 Kelbe, D., Van Aardt, J., Romanczyk, P., Van Leeuwen, M., & Cawse-Nicholson, K. (2015). Single-scan stem reconstruction using low-resolution terrestrial laser scanner data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (7), art. no. 7080835, 3414-3427 Kim, S., McGaughey, R.J., Andersen, H.-E., & Schreuder, G. (2009). Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data. Remote Sensing of Environment, 113 (8), 1575-1586 Kosmidou, V., Petrou, Z., Bunce, R.G.H., Mücher, C.A., Jongman, R.H.G., Bogers, M.M.B., Lucas, R.M., Tomaselli, V., Blonda, P., Padoa-Schioppa, E., Manakos, I., & Petrou, M. (2014). Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system. Ecological Indicators, 36, 290-300 Kötz, B., Schaepman, M., Morsdorf, F., Bowyer, P., Itten, K., & Allgöwer, B. (2004). Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties. Remote Sensing of Environment, 92 (3), 332-344 Kükenbrink, D., Leiterer, R., Schneider, F.D., Schaepman, M.E., & Morsdorf, F. (2015). Voxel based occlusion mapping and plant area index estimation from airborne laser scanning data. Proceedings of the SilviLaser 2015, La Grande Motte, France, 28-30 September 2015 Leiterer, R., Furrer, R., Schaepman, M.E., & Morsdorf, F. (2015a). Towards automated characterization of canopy layering in mixed temperate forests using airborne laser scanning. Forests, 6 (11), 4146-4167 Leiterer, R., Furrer, R., Schaepman, M.E., & Morsdorf, F. (2015b). Forest canopy-structure characterization: A data-driven approach. Forest Ecology and Management, 358, 48-61 Leiterer, R., Muecke, W., Morsdorf, F., Hollaus, M., Pfeifer, N. & Schaepman, M.E. (2013). Operational forest structure monitoring using airborne laser scanning [Flugzeuggestütztes Laserscanning für ein operationelles Waldstrukturmonitoring]. Photogrammetrie, Fernerkundung, Geoinformation, 2013 (3), 173-184 Lim, K., Hopkinson, C., & Treitz, P. (2008). Examining the effects of sampling point densities on laser canopy height and density metrics. Forestry Chronicle, 84 (6), 876-885 Lindberg, E., Eysn, L., Hollaus, M., Holmgren, J., & Pfeifer, N. (2014). Delineation of tree crowns and tree species classification from full-waveform airborne laser scanning data using 3-d ellipsoidal clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (7), art. no. 6849431, 3174-3181

128

Chapter 5

Liu, Y., Zhang, D., Lu, G., & Ma, W.Y. (2007). A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40 (1), 262-282 McElhinny, C., Gibbons, P., Brack, C., & Bauhus, J. (2005). Forest and woodland stand structural complexity: Its definition and measurement. Forest Ecology and Management, 218 (1-3), 1-24 McRoberts, R.E., Næsset, E., Gobakken, T.,& Bollandsås, O.M. (2015). Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data. Remote Sensing of Environment, 164, 36-42 Moffett, M.W. (2000). What’s ‘Up’? A critical look at the basic terms of canopy biology. Biotropica, 32 (4a), 569-596 Montaghi, A. (2013). Effect of scanning angle on vegetation metrics derived from a nationwide airborne laser scanning acquisition. Canadian Journal of Remote Sensing, 39 (SUPPL.1), S152-S173 Næsset, E., & Gobakken, T. (2008). Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment, 112 (6), 3079-3090 Næsset, E. (2005). Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data. Remote Sensing of Environment, 98, 356-370 Næsset, E. (2004). Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scandinavian Journal of Forest Research, 19 (2), 164-179 Ørka, H.O., Næsset, E., & Bollandsås, O.M. (2010). Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning. Remote Sensing of Environment, 114 (7), 1445-1461 Pan, Y., Birdsey, R.A., Phillips, O.L., & Jackson, R.B. (2013). The structure, distribution, and biomass of the world's forests. Annual Review of Ecology, Evolution, and Systematics, 44, 593-622 Parker, G.G., & Brown, M.J. (2000). Forest canopy stratification - is it useful?. The American Naturalist, 155 (4), 473-484 Pesonen, A., Maltamo, M., Eerikäinen, K., & Packalèn, P. (2008). Airborne laser scanning-based prediction of coarse woody debris volumes in a conservation area. Forest Ecology and Management, 255 (8-9), 3288-3296 Russell, M.B., Fraver, S., Aakala, T., Gove, J.H., Woodall, C.W., D'Amato, A.W., & Ducey, M.J. (2015). Quantifying carbon stores and decomposition in dead wood: A review. Forest Ecology and Management, 350, 107-128

129

Synthesis

Schneider, F.D., Leiterer, R., Morsdorf, F., Gastellu-Etchegorry, J.P., Lauret, N., Pfeifer, N., & Schaepman, M.E. (2014). Simulating imaging spectrometer data: 3D forest modelling based on LiDAR and in situ data. Remote Sensing of Environment, 152, 235-250 Skowronski, N.S., Clark, K.L., Gallagher, M., Birdsey, R.A., & Hom, J.L. (2014). Airborne laser scanner-assisted estimation of aboveground biomass change in a temperate oak-pine forest. Remote Sensing of Environment, 151, 166-174 Swetnam, T.L., Falk, D.A., Lynch, A.M., & Yool, S.R. (2014). Estimating individual tree mid- and understory rank-size distributions from airborne laser scanning in semi-arid forests. Forest Ecology and Management, 330, 271-282 Tang, H., Dubayah, R., Brolly, M., Ganguly, S., & Zhang, G. (2014). Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform lidar (GLAS/ICESat). Remote Sensing of Environment, 154, 8-18 Tiede D., Blaschke T., & Heurich M. (2004). Object-based semi-automatic mapping of forest stands with laser scanner and multi-spectral data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVI, Part 8/W2: 328-333 Torabzadeh, H., Leiterer, R., Hueni, A., Schaepman, M.E., & Morsdorf, F. (2015a). Tree species classification in dense temperate mixed forests using a combination of imaging spectroscopy and airborne laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing (in review) Torabzadeh, H., Leiterer, R., Tuia, D., Schaepman, M.E., & Morsdorf, F. (2015b). 3D iterative tree crown delineation in a multi-layered forest. Remote Sensing of Environment (submitted) Treitz, P., Lim, K., Woods, M., Pitt, D., Nesbitt, D., & Etheridge, D. (2012). LiDAR sampling density for forest resource inventories in Ontario, Canada. Remote Sensing, 4 (4), 830-848 Vastaranta, M., Kankare, V., Holopainen, M., Yu, X., Hyyppä, J., & Hyyppä, H. (2012). Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data. ISPRS Journal of Photogrammetry and Remote Sensing, 67 (1), 73-79 Vauhkonen, J., & Imponen, J. (2015). Unsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning. Forestry: An International Journal of Forest Research. (submitted) Vehmas, M., Packalén, P., Maltamo, M., & Eerikäinen, K. (2011). Using airborne laser scanning data for detecting canopy gaps and their understory type in mature boreal forest. Annals of Forest Science, 68 (4), 825-833 Verburg, P.H., Neumann, K., & Nol, L. (2011). Challenges in using land use and land cover data for global change studies. Global Change Biology, 17 (2), 974-989

130

Chapter 5

Villikka, M., Packalén, P., & Maltamo, M. (2012). The suitability of leaf-off airborne laser scanning data in an area-based forest inventory of coniferous and deciduous trees. Silva Fennica, 46 (1), 99-110 Wang, Y., Weinacker, H., & Koch, B. (2008). A Lidar point cloud based procedure for vertical canopy structure analysis and 3D single tree modelling in forest. Sensors, 8 (6), 3938-3951 Whitehurst, A.S., Swatantran, A., Blair, J.B., Hofton, M.A., & Dubayah, R. (2013). Characterization of canopy layering in forested ecosystems using full waveform LiDAR. Remote Sensing, 5, 2014-2036 Wing, B.M., Ritchie, M.W., Boston, K., Cohen, W.B., Gitelman, A., & Olsen, M.J. (2012). Prediction of understory vegetation cover with airborne LiDAR in an interior ponderosa pine forest. Remote Sensing of Environment, 124, 730-741 Yu, X., Hyyppä, J., Holopainen, M., & Vastaranta, M. (2010). Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes. Remote Sensing, 2 (6), 1481-1495

131

Curriculum vitae Academic education 2011-2016

Ph.D. Department of Geography, Remote Sensing Laboratories, University of Zurich (Switzerland). Thesis: Characterization of forestcanopy structure using airborne laser scanning.

2008-2011

M.Sc. in Geoinformatics, Department of Earth Observation, FriedrichSchiller-University Jena (Germany). Thesis: Estimation of potential synergy effects between global LULC products and SAR based GSV data: A case study in China.

2003-2006

B.Sc. in Geography, Department of Earth Observation, FriedrichSchiller-University Jena (Germany). Thesis: Development of a method for cloud detection and cloud masking based on MERIS FR data: A case study in North Africa.

Professional experience 2012-2016

Research assistant, Swiss National Point of Contact for Satellite Images, Remote Sensing Laboratories, University of Zurich (Switzerland).

2006-2011

Research assistant, Department of Earth Observation, Friedrich-SchillerUniversity Jena (Germany).

2005

Internship, JenaOptronik-Aerospace & Security, Jena (Germany).

Teaching and supervision 2012-2015

Tutorage & Lecture assistant (GEO123/GEO233/GEO803), Department of Geography, Remote Sensing Laboratories, University of Zurich (Switzerland).

2012-2013

M.Sc. co-supervision, Fabian Schneider, Simulating Imaging Spectrometer Data: 3D Forest Modeling Based on LiDAR and In Situ Data. (Schneider, F.D., Leiterer, R., Morsdorf, F., Gastellu-Etchegorry, J.-P., Lauret, N., Pfeifer, N. & Schaepman, M.E. (2014). Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sensing of Environment, 152, pp. 235-250)

2004-2006/2010/2011

Tutorage, Department of Earth Observation & Department of Physical Geography, Friedrich-Schiller-University Jena (Germany).

2004-2006

Student assistant, Department of Earth Observation & Department of Physical Geography, Friedrich-Schiller-University Jena (Germany).

133

Curriculum vitae

Services of academic self-government 2012-2016

Member of the Advisory Board of the Graduate School of Geography, University of Zurich (Switzerland).

2005–2006

Elected member of the University Senate and of the Curriculum Committee, Friedrich-Schiller-University Jena (Germany).

2004–2006

Elected member of the Student Representative Council at the Institute of Geography and of the Council of the Faculty of Chemical and Earth Sciences, Friedrich-Schiller-University Jena (Germany).

Graduate courses and professional training o

PhD Seminar I

o

PhD Seminar II

o

Principles and Theories in Geography

o

Retreat Seminar

o

Scientific writing in the Sciences and Medicine

o

Teaching courses (Début & Novice)

o

Project management

o

Voice training and presentation skills

o

Workshops 

Uncertainty in decision making in a changing climate



Point Clouds from ALS and Aerial Images for Vegetation Analysis



Praxiseinsatz von LiDAR und Oberflächenmodellen in der Waldplanung

134

List of publications Peer-reviewed journal papers Leiterer, R., Torabzadeh, H., Furrer, R., Schaepman, M.E. & Morsdorf, F. (2015). Towards automated characterization of canopy layering in mixed temperate forests using airborne laser scanning. Forests, 6 (11), 4146-4167. Leiterer, R., Furrer, R., Schaepman, M.E. & Morsdorf, F. (2015). Forest canopy-structure characterization: A data-driven approach. Forest Ecology and Management, 358, 48-61. Schneider, F.D., Leiterer, R., Morsdorf, F., Gastellu-Etchegorry, J.-P., Lauret, N., Pfeifer, N. & Schaepman, M.E. (2014). Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sensing of Environment, 152, 235-250. Leiterer, R., Muecke, W., Morsdorf, F., Hollaus, M., Pfeifer, N. & Schaepman, M.E. (2013). Operational forest structure monitoring using airborne laser scanning [Flugzeuggestütztes Laserscanning für ein operationelles Waldstrukturmonitoring]. Photogrammetrie, Fernerkundung, Geoinformation, 2013 (3), 173-184. Ling, F., Li, Z., Chen, E., Huang, Y., Tian, X., Schmullius, C., Leiterer, R., Reiche, J. & Santoro, M. (2012). Regional forest and non-forest mapping using Envisat ASAR data. Journal auf Remote Sensing, 16 (5), 1101-1114.

Other scientific publications Escriba, C.G., Yamasaki, E., Leiterer, R., Tedder, A., Shimizu, K., Morsdorf, F. & Schaepman, M.E. (2015). Mapping genetic and phylogenetic diversity of a temperate forest using remote sensing based upscaling methods. Proceedings, AGU, San Francisco, USA, 14-18 December, 1 p. Leiterer, R., Schaepman, M.E. & Morsdorf, F. (2015). Towards automated characterization of horizontal and vertical forest structure using multi-seasonal airborne laser scanning. Proceedings, SilviLaser, La Grande Motte, France, 28-30 September, 3 p. Kükenbrink, D., Leiterer, R., Schneider, F.D., Schaepman, M.E. & Morsdorf, F. (2015). Voxel based occlusion mapping and plant area index estimation from airborne laser scanning data. Proceedings, SilviLaser, La Grande Motte, France, 28-30 September, 3 p. Schneider, F.D., Leiterer, R., Schaepman, M.E. & Morsdorf, F. (2015). Canopy height and plant area index changes in a temperate forest between 2010–2014 using airborne laser scanning. Proceedings, SilviLaser, La Grande Motte, France, 28-30 September, 3 p. Leiterer, R., Furrer, R., Schaepman, M.E. & Morsdorf, F. (2015). Retrieval of canopy structure types for forest characterization using multi-temporal airborne laser scanning. International Geoscience and Remote Sensing Symposium (IGARSS), art. no. 7326357, 2650-2653.

135

List of publications

Torabzadeh, H., Leiterer, R., Schaepman, M.E. & Morsdorf, F. (2015). Optimal structural and spectral features for tree species classification using combined airborne laser scanning and hyperspectral data. International Geoscience and Remote Sensing Symposium (IGARSS), art. no. 7327056, 5399-5402. Leiterer, R., Rinderknecht, P. & Morsdorf, F. (2015). Kantonsweite und einheitliche Klassifikation der horizontalen und vertikalen Waldstruktur. Geomatik Schweiz, 9, 336339. Heisig, H., Jörg, P., Leiterer, R., Wyss, F. & Zesiger, M. (2015). Satellitenbilddaten: Neste Sensoren, erleichterter Datenzugang und innovative Produkte. Geomatik Schweiz, 9, 325330. Schneider, F.D., Leiterer, R., Morsdorf, F. & Schaepman, M.E. (2014). Remote sensing of forest ecosystems using airborne laser scanning and imaging spectroscopy. Proceedings, Swiss Geoscience Meeting, Fribourg, Switzerland, 21-22 November, 1 p. Wulf, H., Joerg, P.C., Leiterer, R. & Schaepman, M.E (2014). New perspectives from Landsat 8 and Sentinel-2: Earth Observation Products. Proceedings, Swiss Geoscience Meeting, Fribourg, Switzerland, 21-22 November, 2 p. Leiterer, R., Morsdorf, F., Furrer, R. & Schaepman, M.E. (2014). Robust characterization of forest canopy structure using Bayesian mixture models. Proceedings, ForestSat, Riva del Garda, Italy, 04-07 November. Schneider, F.D., Leiterer, R., Morsdorf, F., Gastellu-Etchegorry, J.-P., Lauret, N., Pfeifer, N. & Schaepman, M.E. (2014). Discrete Anisotropic Radiative Transfer Simulation of HighDimensional Imaging Spectrometer Data Based on LiDAR and In Situ Data. 4th International Symposium: Recent Advances in Quantitative Remote Sensing, Valencia, Spain, 22-26 September, 1 p. Torabzadeh, H., Morsdorf, F., Leiterer, R. & Schaepman, M.E. (2014). Fusing imaging spectrometry and airborne laser scanner data for tree species discrimination. International Geoscience and Remote Sensing Symposium (IGARSS), art. no. 6946660, 1253-1256. Schneider, F.D., Leiterer, R., Morsdorf, F., Gastellu-Etchegorry, J.-P., Laurent, N., Pfeifer, N. & Schaepman, M.E. (2014). Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Proccedings, Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF), 23, 6 p. Joerg, P.C., Wulf, H., Leiterer, R., Schaepman, M.E., Wyss, F., Zesiger, M. & Bovet, S. (2014). Swiss Earth Observation support for Societal Benefit Areas. Tenth Plenary Session of the Group on Earth Observation (GEO-X), Geneva, Switzerland, 13-16 January. Schaepman, M.E., Morsdorf, F., Leiterer, R., He, Q., Schneider, F., Torabzadeh, H. & Shimizu, K. (2014). Quantitative Biodiversity Measurements using Earth Observations: a Modelling System. Tenth Plenary Session of the Group on Earth Observation (GEO-X), Geneva, Switzerland, 13-16 January.

136

List of publications

Schneider, F.D., Leiterer, R., Morsdorf, F., Gastellu-Etchegorry, J.-P., Laurent, N., Pfeifer, N. & Schaepman, M.E. (2013). Simulating imaging spectrometer data of a mixed old-growth forest: A parameterization of a 3D radiative transfer model based on airborne and terrestrial laser scanning. Proceedings, AGU, San Francisco, USA, 09-13 December, 1 p. Torabzadeh, H., Morsdorf, F., Leiterer, R. & Schaepman, M.E. (2013). Determining forest species composition using imaging spectrometry and airborne laser scanner data. Proceedings, Swiss Geoscience Meeting, Lausanne, Switzerland, 15-16 November, 2 p. Leiterer, R., Morsdorf, F. & Schaepman, M.E. (2013). On the relevance of spatial scale in ALS based forest structure characterizations. Proceedings, SilviLaser, Beijing, China, 09-11 October. Morsdorf, F., Leiterer, R., Schneider, F.D., Schaepman, M.E., Brazile, J., Pfeifer, N., Hollaus, M., Disney, M., Lewis, P., Gastellu-Etchegorry, J.-P. & Koetz, B. (2013). 3D-Vegetation Laboratory: Science and modeling support for accuracy assessment and prototyping of EO data and products. Proceedings, Living Planet Symposium, Edinburgh, UK, 09-13 September. Schmullius, C., Leiterer, R., Burjack, I., Traut, K., Santoro, M., Li, Z.Y. & Ling, F.L. (2013). Forest dragon 2: Final results of the european partners. European Space Agency, (Special Publication) ESA SP, (SP-704), 10 p. Ling, F., Li, Z., Chen, E., Huang, Y., Tian, X., Schmullius, C., Leiterer, R., Reiche, J. & Santoro, M. (2013). Forest and non-forest mapping with envisat asar images. European Space Agency, (Special Publication) ESA SP, (SP-704), 8 p. Torabzadeh, H., Morsdorf, F., Leiterer, R. & Schaepman, M.E. (2013). Mapping tree species composition using imaging spectrometry and airborne laser scanning data. Proceedings, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W3, SMPR, Teheran, Iran, 05-08 October, 4 p. Leiterer, R., Morsdorf, F. & Schaepman, M.E. (2013). Characterization of forest understory using multi-temporal full-waveform airborne laser scanning. Proceedings, Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF), 22, 384-390. Schaepman, M.E., Morsdorf, F., Leiterer, R., Pfeifer, N., Hollaus, M., Disney, M., Lewis, P., Gastellu-Etchegorry, J.-P., Brazile, J. & Koetz, B. (2012). Novel reference site approach to prototyping, calibrating, and validating Earth observation data and products. Proceedings, AGU, San Francisco, USA, 03-07 December, 1 p. Leiterer, R., Morsdorf, F. & Schaepman, M.E. (2012). High resolution retrieval of forest canopy structure using multi-temporal airborne laser scanning. Proceedings, Swiss Geoscience Meeting, Bern, Switzerland, 16-17 November, 2 p. Leiterer, R., Morsdorf, F., Schaepman, M.E., Muecke, W., Pfeifer, N. & Hollaus, M. (2012). 3D Vegetationskartierung: flugzeuggestütztes Laserscanning für ein operationelles Waldstrukturmonitoring”, AK Fernerkundung, Bochum, Germany, 04-05 October, 10 p.

137

List of publications

Leiterer, R., Morsdorf, F., Schaepman, M.E., Hollaus, M. & Pfeifer, N. (2012). Radiative transfer modeling for vegetation canopies – ALS/TLS data integration. Vegetation Analysis Workshop, Vienna, Austria, 24-25 September 2012. Leiterer, R., Morsdorf, F., Schaepman, M.E., Muecke, W., Hollaus, M. & Pfeifer, N. (2012). Robust characterization of forest canopy structure types using full-waveform airborne laser scanning. Proceedings, SilviLaser, Vancouver, Canada, 16-19 September, 8 p. Eysn, L., Muecke, W., Ressl, C., Hollaus, M., Leiterer, R., Blauensteiner, F. & Pfeifer, N. (2012). Investigations on the orientation of terrestrial laser scanning point clouds in dense forests. Proceedings, SilviLaser, Vancouver, Canada, 16-19 September. Morsdorf, F., Leiterer, R., Schaepman, M.E., Pfeifer, N., Hollaus, M., Lewis, P., Disney, M., Gastellu-Etchegorry, J.-P., Brazile, J. & Koetz, B. (2012). A scientific support tool for accuracy assessment and prototyping of EO data and products. Proceedings, ForestSat, Corvallis, USA, 11-14 September. Leiterer, R., Morsdorf, F., Torabzadeh, H., Schaepman, M.E., Muecke, W., Pfeifer, N. & Hollaus, M. (2012). A voxel-based approach for canopy structure characterization using fullwaveform airborne laser scanning. International Geoscience and Remote Sensing Symposium (IGARSS), art. no. 6350691, 3399-3402. Hochschild, V., Kropacek, J., Biskop, S., Braun, A., Chen, F., Fink, M., Helmschrot, J., Kang, S., Krause, P., Leiterer, R., Ye, Q. & Fluegel, W.-A. (2012). Multi-sensor remote sensing based modelling of the water balance of endorheic lakes on the Tibetan Plateau. IAHS-AISH Publication, 352, 253-256. Morsdorf, F., Leiterer, R., Schaepman, M.E., Pfeifer, N., Hollaus, M., Disney. M., Lewis, P., Gastellu-Etchegorry, J.-P., Brazile, J. & Koetz, B. (2012). 3D-VegetationLab: Scientific support for accuracy assessment and prototyping of EO data and products. Proceedings, Sentinel-2 Prepatory Symposium, Frascati, Italy, 23-27 April. Leiterer, R., Herold, M., Santoro, M. & Schmullius, C. (2011). Vergleichende Validierung globaler Landnutzungs/ Landbedeckungs-Produkte am Beispiel China’s. Proceedings, AK Fernerkundung, Würzburg, Germany, 29-30 September. Ling, F., Leiterer, R., Huang, Y., Santoro, M., Chen, E., Li, Z. & Schmullius, C. (2011). Forest cover change mapping in northeast China using ERS SAR and ENVISAT ASAR data. Proceedings, Dragon 2 Symposium, Prague, Czech Republic, 20-24 June 2011. Leiterer, R., Santoro, M. & Schmullius, C. (2011). Estimation of synergies between SAR based forest stem volume maps and global land cover products. Proceedings, Dragon 2 Symposium, Prague, Czech Republic, 20-24 June 2011. Ling, F., Leiterer, R., Huang, Y., Reiche, J. & Li, Z. (2011). Forest change mapping in northeast China using SAR and INSAR data. Proceedings, 34th International Symposium on Remote Sensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring, 3 p.

138

ISBN Nr. 978-3-9524551-7-3