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Remote Sensing Laboratories Doc. Ref: Dept. of Geography Version: 1.0 University of Zurich Date: 30/06/2011 Winterthurerstrasse 190 Page: 1 of 24 CH – 8057 Zurich File Name: 3DVegLab_FieldProtocol_Final.docx

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Field Protocols Document

Title: 3D Vegetation Lab (3DVegLab) - Field Protocol Document

Document reference RSL/FP-3DVegLab/0.b

Project reference: Parent project: RSL/AO/1-652910/I-NB

Prepared by: Felix Morsdorf, Reik Leiterer, Matt Disney, Jean-

Philippe Gastellu-Etchegorry, Philip Lewis, Norbert

Pfeifer, Markus Hollaus, Michael Schaepman.

Signature:

Date: 2011-06-08

Authorised by: Michael Schaepman

Signature:

Issued by:

Sector/location/telephone/fax:

Remote Sensing Laboratories

Department of Geography

University of Zurich

Winterthurstrasse 190

CH-8057 Zurich

Abstract

This document presents the variable list, sampling approach, field protocols and field manuals to be used in

the three-dimensional scene characterization and in-situ campaigns in the 3D Vegetation Laboratory project.



Record of changes

This is a controlled document. Additional copies are obtained through the issuing authority. In the event of

copying locally, including electronically, mark each document ‘Uncontrolled Copy’. Full issues and page

amendments are identified on this page. Proposals for change should be made in writing to the issuing

authority.

Issue Date Detail of changes

0.a

0.b

0.c

8 June 2011

29 June 2011

30 June 2011

First draft for discussion at technical progress meeting

Second draft for approval of TO

Final draft



Table of contents

1 INTRODUCTION 5

1.1 Contractual 5

1.2 Purpose of the Document 5

1.3 Background 5

1.4 Links with CEOS land product validation (LPV) standards 5

1.5 Definitions, acronyms and abbreviations 6

1.6 References 7

2 LIST OF VARIABLES 9

3 SAMPLING STRATEGIES 10

3.1 In-situ data 10

3.2 Terrestrial laser scanning 11

3.3 Auxiliary information 12

3.4 Airborne laser scanning 12

4 3D VEGETATION LABORATORY FIELD PROTOCOLS 14

4.1 Master field protocol 14

4.2 Structural properties (gap fraction & LAI) 14

4.3 Field mapping system (tree locations & dimensions) 14

4.4 Optical properties 14

4.5 Structural reconstruction (TLS) 14

4.6 Auxiliary information (FLUXNET) 14

4.7 Radiation (sun-photometer) 14

4.8 Tachymeter 15

5 APPENDIX: FIELD PROTOCOLS 16

5.1 Master document 16

5.2 ASD FieldSpec/ SPAD 17

5.3 FieldMapSystem 18

5.4 Hemispherical Photography/ LAI2000 19



5.5 Terrestrial Laser Scanning 20

5.6 Surveying 21

5.7 dGPS 22

5.8 MFR SunPhotometer 23

5.9 FLUXNET station 24



1 Introduction

1.1 Contractual

This field protocol document has been issued by RSL for ESA/ESRIN under contract AO/1-6529/10/I-NB.

1.2 Purpose of the Document

This document provides the field protocols and sampling designs for the 3D Vegetation Lab (3DVegLab)

software tool, which will be used to simulate data of passive optical instruments of two well-characterized

test sites. We will provide reasoning for the development of a proper sampling strategy for each variable and

list the field protocols for the used instrumentation in the appendix of this document. While this document is

the basis of the campaign planning for the three-dimensional characterizations of the chosen sites, it should

as well be usable by other scientific groups, which would like to use their vegetation site with the 3D

vegetation laboratory toolbox.

This document will introduce the variables to be measured (Section 2), the respective sampling strategies and

data used to define stratified sampling strategies (Sections 3,3.2 and 3.3) and then the field protocols (Section

4) and practical instrument manuals (Section 5).

1.3 Background

The role of the 3DVegLab (3D Vegetation Lab) software is to permit the assessment of the capabilities of

upcoming ESA missions (as e.g. Sentinel 2/3) for advanced vegetation products. Hence consideration needs

to be given to environmental factors that affect biological matter (especially vegetation) and equipment, both

in the generation of the extensive field data sets and scope and functionalities of the software modules.

When being used for calibration/validation, the 3DVegLab set of tools will allow for detailed simulations to

generate passive optical imagery of varying spatial and spectral resolution. As part of the BEAM system, the

3DVegLab modules will be able to export its results directly into various pre-existing simulation tools.

It is intended that the users of 3DVegLab should ultimately be mission engineers and planners, as well as

scientists interested in prototyping new retrieval algorithms and vegetation products. However, it is noted

that the software is, at the request of the Customer, to be based on the BEAM toolkit, which is more

accurately considered as an advanced research tool.

1.4 Links with CEOS land product validation (LPV) standards

3DVeglab is linked to the Action Item T29 of the GCOS Implementation Plan 2010 [27] with regard to the

contribution to the establishment of a global network of ecological monitoring sites with emphasis on LAI

and fAPAR. In the following the action item T29 and it's role in the 3D VegLab field sites and protocols is

defined in more detail.

Action T29 [IP-04 T29]

The aim of the action item T29 is to establish a calibration/validation network of in situ reference sites for

FAPAR and LAI and conduct systematic, comprehensive evaluation campaigns to understand and resolve

differences between the products and increase their accuracy. One existing pilot project of the CEOS WGCV

LPV (Land Product Validation Group) with particular relevance to 3DVegLab is the LAI inter-comparison

from different sensors (with varying spatial and spectral resolution). It is envisaged that similar studies are

possible using the 3DVegLab toolbox by its unique combination of EOD, RTMs and field data.

Additionally, several activities of the 3DVegLab are in concordance or overlap with CEOS LPV activities:



The 3DVegLab toolbox and scientific studies are linked to some of the LPV related Essential

Climate Variables LAI, fAPAR, Biomass, Land Cover of the ESA Climate Change Initiative and

will be provided either by field measurements and/or the 3D scene analysis component.

The 3DVegLab land cover description will be performed according to the FAO Land Cover

Classification System (LCCS).

The 3DVegLab validation approach will include stage 1 & 2 validation according to CEOS / LPV

recommendations and is oriented to the Quality Assurance Framework for Earth Observation

(QA4EO) of the ESA/CEOS Cal/Val initiative.

1.5 Definitions, acronyms and abbreviations

AGL Above Ground Level

ALS Airborne Laser Scanning

BEAM An open-source toolbox and development platform for viewing, analyzing and processing of

remote sensing data.

BRF Bidirectional reflectance factor

CEOS Committee on Earth Observation Satellites

DSM Digital Surface Model

DTM Digital Terrain Model

EOD Earth Observation Dataset

ESA European Space Agency

ESRIN European Space Research Institute

DHP Digital Hemispherical Photography

ISD In-Situ Dataset

LPV Land Product Validation

RTM Radiative Transfer Model

TLS Terrestrial Laser Scanning

TOA Top-of-atmosphere

TOC Top-of-canopy

URD User Requirements Document



1.6 References

[1] ECSS-E-40 Part 1B: Space engineering - Software - Part 1: Principles and requirements

[2] ECSS-E-40 Part 2: space engineering - Software - Part 2: Document requirements definitions (DRDs)

[3] Statement of work: 3D Vegetation Lab (STSE-LAND-EOPG-SW-10-0001)

[4] Proposal: RSL 3DVegLab Management Proposal

[5] Proposal: RSL 3DVegLab Technical Proposal

[6] Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory, Köhl, M., Magnussen, S.,

Marchetti M. 2006, ISBN: 3-540-32571-9

[7] Chasmer, L., N. Kljun, C. Hopkinson, S. Brown, T. Milne, K. Giroux, A.G. Barr, K. Devito, R. Petrone, 2011:

Characterizing vegetation structural and topographic characteristics sampled by eddy covariance within two

mature aspen stands using LiDAR and a flux footprint model: Scaling to MODIS. Journal of Geophysical

Research - Biogeosciences, in press.

[8] Hilker, T., F.G. Hall, N.C. Coops, A. Lyapustin, Y. Wang, N. Grant, Z. Nesic, T.A. Black, N. Kljun, L.

Chasmer, C. Hopkinson, 2010: Remote Sensing of Photosynthetic Light Use Efficiency Across two Forested

Biomes: Spatial Scaling. Remote Sensing of Environment, 114, 2863-2874.

[9] Naesset, E. & T. Okland (2002): Estimating tree height and tree crown properties using airborne scanning laser

in a boreal nature reserve. - Remote Sensing of Environment, 79, (1), 105-115.

[10] Yu1, X, Hyyppä1, J., Hyyppä, H. & M. Maltamo (2004): Effects of flight altitude on tree height estimation

using airborne laser scanning. - International Archives of Photogrammetry, Remote Sensing and Spatial

Information Sciences, XXXVI - 8/W2, 96-101.

[11] Hollaus, M., Wagner, W., Eberhofer, C. & W. Karel (2006): Accuracy of large-scale canopy heights derived

from LiDAR data under operational constraints in a complex alpine environment. - ISPRS Journal of

Photogrammetry and Remote Sensing, 60, (5), 323-338.

[12] Avery, E. (1967): Forest Measurement. McGraw-Hill: New York.

[13] Köhl, M., Magnussen, S. & M. Marchetti (2006): Sampling Methods, Remote Sensing and GIS Multiresource

Forest Inventory. Springer: Berlin, Heidelberg, New York.

[14] Schreuder, H. T., Gregoire, T. G. & G.B. Wood (1993): Sampling methods for multiresource forest inventory.

John Wiley and Sons: New York, USA.

[15] Maas, H.-G., Bienert, A., Scheller, S. & E. Keane (2008): Automatic forest inventory parameter determination

from terrestrial laser scanner data. - International Journal of Remote Sensing, 29, (5), 1579-1593.

[16] Morsdorf, F., Marell, A., Koetz, B., Cassagne, N., Pimont, F., Rigolot, E. & B. Allgower (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.

[17] Lovell, J.L., Jupp, D.L.B., Newnham, G.J. & D.S. Culvenor (2011): Measuring tree stem diameters using

intensity profiles from ground-based scanning lidar from a fixed viewpoint. - ISPRS Journal of




[18] Jonckheere, I., Nackaerts, K., Muys, B. & P. Coppin (2005): Assessment of automatic gap fraction estimation

of forests from digital hemispherical photography. - Agricultural and Forest Meteorology, 132, (1-2), 96-114.

[19] Leblanc, S.G., Chen, J.M., Fernandes, R., Deering, D.W. & A. Conley (2005): Methodology comparison for

canopy structure parameters extraction from digital hemispherical photography in boreal forests. - Agricultural

and Forest Meteorology, 129, I(3-4), 187-207.

[20] Weiss, M., Baret, F., Smith, G.J., Jonckheere, I. & P. Coppin (2004): Review of methods for in situ leaf area

index (LAI) determination: Part II. Estimation of LAI, errors and sampling. - Agricultural and Forest

Meteorology, 121, (1-2), 37-53.

[21] Cote, J-F., Fournier, R.A. & R. Egli (2011): An architectural model of trees to estimate forest structural

attributes using terrestrial LiDAR. - Environmental Modelling & Software, 26, (6), 761-777.

[22] Milton, E.J., Schaepman, M.E., Anderson, K., Kneubuhler, M. & N. Fox (2009): Progress in field

spectroscopy. - Remote Sensing of Environment, Imaging Spectroscopy Special Issue, 113, (1), Imaging 92-

109.

[23] Houborg, R. & E. Boegh (2008): Mapping leaf chlorophyll and leaf area index using inverse and forward

canopy reflectance modeling and SPOT reflectance data. - Remote Sensing of Environment, 112, (1), 186-202.

[24] Atzberger, C., Jarmer, T., Schlerf, M., Kötz, B. & W. Werner (2003): Spectroradiometric determination of

wheat bio-physical variables: comparison of different empirical-statistical approaches. In: Goossens, R. (Eds.),

Remote Sensing in Transitions, Proc. 23rd EARSeL symposium, Belgium, 2–5 June 2003, pp. 463–470.

[25] Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., Corsi, F. & M. Cho (2008): LAI and chlorophyll

estimation for a heterogeneous grassland using hyperspectral measurements. - ISPRS Journal of


[26] 3D Vegetation Laboratory, Requirement Baseline, Deliverable to ESA within Project RSL/AO/1-652910/I-

NB, Version 0.c, final draft, 24.6.2011

[27] GCOS-138 (2010): Implementation plan for the global observing system for climate In support of the

UNFCCC (2010 update), August 2010, http://www.wmo.int/pages/prog/gcos/Publications/gcos-138.pdf.

[28] Morsdorf, F.; Kötz, B.; Meier, E.; Itten, K. & Allgöwer, B., Estimation of LAI and fractional cover from small

footprint airborne laser scanning data based on gap fraction, Remote Sensing of Environment, 2006, 104, 50-

61.

[29] Morsdorf, F.; Meier, E.; Kötz, B.; Itten, K. I.; Dobbertin, M. & Allgöwer, B., LIDAR-based geometric

reconstruction of boreal type forest stands at single tree level for forest and wildland fire management, Remote

Sensing of Environment, 2004, 3, 353-362.

[30] Morsdorf, F.; Frey, O.; Meier, E.; Itten, K. I. & Allgöwer, B., Assessment of the influence of flying altitude

and scan angle on biophysical vegetation products derived from airborne laser scanning, International Journal

of Remote Sensing, Taylor & Francis, 2008, 29, 1387-1406.

[31] Ahokas, E.; Yu, X.; Oksanen, J.; Hyyppä, J.; Kaartinen, H. & Hyyppä, H.

Vosselman, G. & Brenner, C. (ed.), Optimization of the scanning angle for countrywide laser scanning,

International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2005, XXXVI,

PART 3/W19.

http://www.wmo.int/pages/prog/gcos/Publications/gcos-138.pdf



2 List of Variables

The 3D Vegetation Laboratory will establish two well-characterised sites to provide (among other things, see

[26] for details) a tool for calibration and validation of upcoming satellite missions. These site

characterisations will be established by a combination of both remote and in-situ assessments, using both

remote sensing instruments (e.g. ALS/TLS) as well as specialised instrumentation (e.g. ASD/SPAD). The in-

situ campaigns need to provide the basic data for the three-dimensional descriptions of the established sites,

which will be achieved by a combination of laser scanning across a range of scales [26]. This structural data

will be complemented by leaf optical properties and other variables gathered at different scales for the

characterization of the forest structure (such as LAI and fractional cover) and to provide auxiliary

information needed to parameterize the forest scene (e.g. soil moisture at site scale). Some of these

measurements will be used to validate of the geometric reconstruction of the forest scene, e.g. digital

hemispherical photographs, that can be simulated from the used RT models (see use case iv in [26]). From

the requirements in [26], for most variables, we can derive needed accuracies. For other variables, we use the

best possible solution based on literature and provide those accuracies as is. In some cases, the obtainable

accuracies might as well depend on site characteristics (e.g. geolocation in dense canopies). In the table

below, we provide the list of variables that will be measured on the sites, along with the purpose and needed

accuracies.

Variable Method(s) Scale(s) Sampling Expected Error

tree height

References: [9-16]

Hypsometer

ALS

60 x 60 m PSU

300 x 300 m full

exhaustive (all)

exhaustive (all)

± 0.5 m

± 1.0 m

tree location

References: [15-17]

ALS

dGPS

TLS

300 x 300 m full

20 x 20 m SSU

20 x 20 m SSU

exhaustive (all)

exhaustive (all)

exhaustive (all)

± 2.0 m

± 1.5 m

± 0.1 m

dbh

References: [12-14,17]

Tape

TLS

60 x 60 m PSU

20 x 20 m SSU

exhaustive (all)

exhaustive (all)

< 5 %

< 5 %

LAI

References: [18-20]

Hemispherical Photographs

LAI 2000

20 x 20 m SSU

20 x 20 m SSU

Valeri/NFI plot scheme

Valeri/NFI plot scheme

< 20%

fractional cover

References: [15,16,18,19,20]

ALS

Hemispherical Photographs

300 x 300 m full

20 x 20 m SSU

exhaustive (all)

Valeri/ NFI plot scheme

< 10 %

3D structure

References: [15,16,21,26]

TLS 20 x 20 m SSU Valeri/NFI plot scheme,

individuals

-

crown dimensions (height &

diameter)

References: [9,15,16]

ALS

Field measurements

300 x 300 m full

20 x 20 m SSU

exhaustive (all)

exhaustive (all)

< 10 %

spectral properties of foliage/

background objects

(understory/ soil)

References: [22-25]

ASD, Integrating Sphere 20 x 20 m SSU Valeri/NFI plot scheme,

canopy stratification

< 10 %

biochemical leaf properties

(chlorophyll, water, dry matter

content)

References: [22-25]

Laboratory analysis/indirect

field measurements

SPAD 500

20 x 20 m SSU,

selected trees

20 x 20 m SSU,

selected trees

leaf samples, canopy

stratification

leaf samples, canopy

stratification

< 10 %



3 Sampling strategies

3.1 In-situ data

Of great importance is to choose a proper sampling strategy to capture the spatial (3D) distribution of

structural, optical and biochemical properties within the vegetation canopy. Through the advance of remote

sensing technologies we now know that a forest canopy, which might appear homogenous based on a

qualitative assessment of an aerial photograph, does not need to be homogenous when it comes to 3D

structure or the spatial distribution of processes such as photosynthesis or gas exchange [7,8]. Since the

forest canopy will always be undersampled by field work (as time and costs are prohibiting factors), we

propose a stratified sampling scheme, which will be developed using high-resolution remote sensing data in

the design process. An example of such a dataset, which can be used to assess the three-dimensional

structure of the forest canopy, is depicted in Figure 1. The image shows a RGB composite of three LiDAR

based vegetation height percentiles and one is able to clearly separate stands of different age and

composition. The data that will be available in for both campaign planning and validation will as well be

listed in the SDS deliverable.

Other aspects that will be relevant are the distribution of plant optical parameters and small- scale structures

(e.g. shoot structure), and these are most often linked to tree species. Thus, tree species (via texture) is

another important parameter for the stratification of the sampling approach as presented in Figure 2. This

approach will be applied to the test sites chosen by the 3D VegLab consortium and will be adapted as

necessary.

Figure 1 : RGB composite of LiDAR height percentiles of the forest at the Lägeren site, Switzerland. R is the

0%, G the 50 % and B the 100% height percentile. Different stands are clearly distinguishable based on

horizontal and vertical structure differences. The location of the flux tower is marked with a red circle. This

data (among others) will be used to plan the stratified sampling approach.



Figure 2: Sampling design (to be adapted for the chosen sites, 3DVegLab, after Köhl et al., 2006).

3.2 Terrestrial laser scanning

Terrestrial laser scanning will capture the 3D structural characteristics of the plot. Field measurements are

performed at a number of positions (stand points), which typically take between 5 to 30 minutes per stand

point, depending on the level of detail captured. Precise geolocation of the field measurements is necessary,

because measurements from different stand points are integrated for describing the structure of each single

tree. Extracting the structure explicitly up to level 2 requires (see UR) a number of scans per tree, but on the

other hand each stand point can contribute to modeling of more than one tree. Emphasis is to be laid

especially on those parts of the forest that show the least regularity, i.e., that contribute most to the overall

heterogeneity, as it can be assessed from a ground perspective (level 0 and 1).

The sampling scheme of the 3D structure needs to assure that a minimal level of complexity is provided for

the entire plot and reaches, by a gradual increase, higher level of complexity at selected spots (stratification

w.r.t. complexity). Selection of such a spot requires knowledge beyond the geometrical modeling expertise

and will be based on the analysis done for the stratified sampling in Section 3.

Fig. 3a shows a sampling scheme in the forest, which guarantees that the scans can be oriented to each other

(registration) while simultaneously covering each stem fully. This allows next to location and direction of the



stem also precise BDH determination and level 1 reconstruction, which, however, cannot be guaranteed to be

complete.

Fig. 3b shows a sampling scheme which illustrates the gradual densification of measurements, providing

level 2 reconstruction in the centre.

a) b)

Figure 3: Sampling design for Terrestrial Laser Scanning

3.3 Auxiliary information

The sampling of the auxiliary information needed to a) parameterize the atmospheric radiative regime (using

libradtran) and to b) provide additional information on temporally changing scene properties (such as soil

moisture) is driven by the availability of networks that perform such measurements on a regular basis. As

was asked in the SoW, the final site selection focused on sites that are part of the FLUXNET network. The

measurements performed at the fluxtowers with the protocols driven by the respective networks/projects (e.g.

Carboeurope) will form the basis of the auxiliary data gathered for the 3D Veg Lab project. The larger

advantage of the FLUXNET sites are that i) the measurements are standardized by protocols, ii) the data is

measured continually throughout a year, iii) is openly available for scientific exploitation and iv) is

distributed through a standardized web interface. Thus, the sampling strategy for the auxiliary information

will be based on the spot measurements of the flux tower, with the assumption that this spot measurement is

representative for the chosen site (300 by 300 meters). Furthermore, the data of the flux towers can be used

to assess the temporal variability of the auxiliary parameters around the site. The instrumentation of the two

sites can be found below and the sampling protocols in Section 4.6.

1) Laegeren : http://www.fluxnet.ornl.gov/fluxnet/sitepage.cfm?SITEID=308

2) Tharandt : http://www.fluxnet.ornl.gov/fluxnet/sitepage.cfm?SITEID=404

3.4 Airborne laser scanning

Several structural canopy variables can be derived from airborne laser scanning as well as from TLS and

field measurements [28,29]. While the field based sampling is normally point based, ALS enables an area

and/or single tree based derivation of these variables. Within the 3DVegLab dataset, these structural

variables will be derived at least for the 300 x 300 meter core site (for manual derived variables) or to a

larger extent (max: extent of ALS dataset) for automatically derived variables. These extended datasets can

then be used for cross-validation purposes (e.g. LAI). There are some minimum requirements for the quality

of the small-footprint (< 1 m) ALS data based on [28,29]:

The point density of the ALS data should be higher than 4 points per meter square, to allow for the

derivation of single-tree geometries (either manual or automatic).

The system should at least record first and last echo, if possible multiple returns or the full-

waveform. The full-waveform data is not a requirement for the scene reconstruction, but an added

value for the internal validation of the 3D scene.



Large scanning angles (outside of ± 15º) should be avoided, as occlusion can severely impact

products based on gap fraction [30,31].

Large flying altitudes (> 1000 AGL) should be avoided, these will decrease the accuracy of

parameter retrieval [30,31].

For a deciduous site, the ALS data should be multi-temporal (leaf-on/off), since only this

combination allows for the accurate determination of tree heights and canopy closure variables. All

leaf-on echo heights should be transformed into above-ground-heights using the DTM of the leaf-off

campaign.

In Figure 2:, we present a point cloud of ALS (around the flux-tower at Lägeren) and indicate the variables

that are to be derived from the point cloud. The methods for the respective variables can be found in [28,29].

Figure 2: Point cloud of two ALS campaigns close to the Lägeren fluxtower, Switzerland. Leaf-on data is

colored, while leaf-off data is gray-scale. Variables to be derived are depicted according to their scales of

measurement (either single tree or stand-wise).



4 3D Vegetation Laboratory field protocols

Since the field protocols use their own formatting, we decided to place these documents as hyperlinks in this

deliverable. In the following sections, these links are presented with a brief introduction into the purpose of

these documents. The protocols will be made available with the deliverable to all partners through the project

webpage.

4.1 Master field protocol

This is the master field protocol for each site/campaign, which hierarchically organizes all other field

protocols and contains information about the site, the chosen sampling units and links the following

specialized protocols.

FP_Master

4.2 Structural properties (gap fraction & LAI)

Mainly for the validation of the structural reconstruction, we will obtain stand-wise estimates of LAI and gap

fraction based on indirect in-situ measurements, either by DHP or by using an LAI2000. The former is

preferred, if available.

FP_HemisphericalPhotography_LAI2000

4.3 Field mapping system (tree locations & dimensions)

This protocol is intended for the field observables such as tree locations, heights and diameter at breast

height.

FP_FieldMapSystem

4.4 Optical properties

Leaf, bark and soil optical properties will be measured using ASD FieldSpecs and SPAD502 instruments.

FP_ASD_SPAD502

4.5 Structural reconstruction (TLS)

The three-dimensional structure of the vegetation will be measured by TLS. The protocol will contain

locations, perspectives (horizontal/vertical) and system settings used in the field and will link the

observations with a file name of the scanner.

FP_TLS

4.6 Auxiliary information (FLUXNET)

These protocols contain the observables provided by the FLUXNET observation towers and the respective

additional measurements and PI's (as a point of contact) of the sites. Please note that for Laegeren, missing

additional radiation measurements will be provided by the project.

FP_FLUXNET

4.7 Radiation (sun-photometer)

This protocol will provide ancillary information (time, location, weather, etc.) for the sun photometer

measurements.



FP_MFRSunPhotometer

4.8 Tachymeter

This protocol will provide the basis for the measurements of the Tachymeter, which will provide the TLS

scan locations and single tree locations for validation of the ALS based approach.

FP_Tachymeter



5 Appendix: Field Protocols

5.1 Master document



5.2 ASD FieldSpec/ SPAD



5.3 FieldMapSystem



5.4 Hemispherical Photography/ LAI2000



5.5 Terrestrial Laser Scanning



5.6 Surveying



5.7 dGPS



5.8 MFR SunPhotometer



5.9 FLUXNET station

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