1

Technical University Berlin

School IV – Planning Building Environment

Institute of Landscape Architecture and Environmental Planning

Department of Geoinformation in Environmental Planning

Estimation of Seasonal Leaf Area Index

in an Alluvial Forest Using High Resolution

Satellite-based Vegetation Indices

by

Adina Tillack

A thesis submitted in partial fulfillment of the requirements for the degree of

Master of Science

in Environmental Planning

August 2012

1. Supervisor: Prof Dr. Birgit Kleinschmit

2. Supervisor: Dipl.-Ing. Anne Clasen

Adina Tillack Erieseering 7

1019 Berlin Matr.nr.: 311388

Email: adinatill@gmx.de Tel.: 0160 1536874

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Author’s Declaration / Eidesstattliche Erklärung

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Author’s Declaration / Eidesstattliche Erklärung

I hereby certify that I am the sole author of this master thesis. Furthermore, I confirm that

no sources have been used in the preparation of this thesis other than those indicated in the

thesis itself. The works of other people included in my thesis, published or otherwise, are

fully acknowledged in accordance with the standard referencing practices.

This thesis has not been submitted for another degree or master to any other University or

Institution.

Hiermit versichere ich, dass ich die vorliegende Arbeit selbstständig verfasst und keine

anderen als die angegebenen Quellen und Hilfsmittel benutzt habe. Alle Ausführungen, die

anderen veröffentlichten oder nicht veröffentlichten Schriften wörtlich oder sinngemäß

entnommen wurden, habe ich kenntlich gemacht.

Die Arbeit hat in gleicher oder ähnlicher Fassung noch keiner anderen Prüfungsbehörde

vorgelegen.

____________________________ ____________________________

Date / Datum Signature / Unterschrift

Summary

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Summary

In preparation of the German hyper spectral EnMAP satellite mission, a joint project exists

between the technical universities of Freiburg, Göttingen and Berlin estimating forest bio-

physical, biochemical and structural attributes with procedures of image-guided spectros-

copy. The Technical University Berlin focuses on the analysis of the biodiversity-

monitoring in alluvial forests. The project is called “ForestHype – Hyper spectral data to

characterize forest attributes”.

As contribution to this research project the master thesis deals with the estimation of the

leaf area index (LAI) in black alder stands, as an indicator for biodiversity. This index is a

key feature of the canopy structure and therefore very important when monitoring forest

dynamics. The main objective is the validation of relationships between field measured

LAI and four satellite derived spectral vegetation indices (SVI) using multi-temporal data.

Therefore, forest dynamics were analyzed by four self-defined phenological phases and

over the whole vegetation period of 2011 (April to November).

Ground measurements were made with the LI-COR 2200 plant canopy analyzer (PCA) and

serve as reference data for the four SVI. The following SVI were derived from different

high resolution RapidEye scenes: the normalized different vegetation index (NDVI), the

red edge NDVI (NDVI-RE), the modified red edge simple ratio (mSR-RE), and curvature.

After the geospherically and atmospherically correction of RapidEye data, the SVI were

computed for each sample plot and date, pixel based. The coefficient of determination (R²)

was used to compare the different relations between field and satellite derived LAI.

The results of this master thesis show seasonal variations of LAI-SVI relationships in the

four phenological phases and over the whole vegetation period. Each phase was dominated

by a different SVI. Especially the importance of the RapidEye incorporated red edge chan-

nel in terms of less LAI decrease or increase was revealed. Thus, multi-temporal estima-

tions of LAI are fundamental, because of different environmental influences and changing

phenology throughout the year.

Zusammenfassung

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Zusammenfassung

In Vorbereitung auf den Start der deutschen hyperspektralen EnMAP Sattelitenmission

besteht ein Verbundprojekt zwischen den Technischen Universitäten Freiburg, Göttingen

und Berlin, um verschiedene biophysikalische, biochemische und strukturelle Merkmale

von Wäldern mittels Verfahren der bildgebenden Spektroskopie zu untersuchen. Die Tech-

nische Universität Berlin hat ihren Schwerpunkt beim Biodiversitäts-Monitoring in Auen-

wäldern. Der Projekttitel lautet „ForestHype – Hyperspektraldaten zur Charakterisierung

von Waldmerkmalen“.

Die Masterarbeit ist ein Beitrag zu diesem Forschungsprojekt und behandelt die Ableitung

des Blattflächenindex (LAI) in Schwarzerlenbeständen, einen wichtigen Indikator für die

Biodiversität. Dieser Index ist ein Schlüsselmerkmal der Vegetationsstruktur und somit für

das Monitoring von Wäldern sehr wichtig. Das Hauptziel dieser Arbeit ist die Validierung

der Zusammenhänge zwischen Feldmessungen des LAI und vier satteliten-basierter spekt-

raler Vegetationsindizes (SVI), unter Nutzung multi-temporaler Daten. Aus diesem Grund

wurde die Walddynamik über vier selbst definierte phänologische Phasen und die gesamte

Vegetationsperiode des Jahres 2011 untersucht (April bis November).

Die Feldmessungen fanden unter Verwendung des LI-COR 2200 Plant Canopy Analyzer

(PCA) statt und dienten den vier SVI als Referenzdaten. Die folgenden SVI wurden von

verschiedenen hochaufgelösten RapidEye Szenen abgeleitet: der normalisierte differenzier-

te Vegetationsindex (NDVI), der Red-Edge NDVI (NDVI-RE), der modifizierte Red-Edge

Simple Ratio (mSR-RE) und die Krümmung (Curvature). Nach der Geo-, sowie Atmo-

sphärenkorrektur der RapidEye Daten, wurden die SVI pixelbasiert für jeden Messpunkt

und jedes Datum berechnet. Das Bestimmtheitsmaß (R²) diente dem Vergleich der Zu-

sammenhänge zwischen im Feld gemessenen und Satellitenbild-abgeleiteten LAI-Werten.

Die Ergebnisse der Masterarbeit zeigen saisonale Unterschiede der LAI-SVI Zusammen-

hänge in den vier phänologischen Phasen, sowie über die gesamte Vegetationsperiode.

Jede Phase wurde von anderen Indices dominiert. Besonders in Zeiten mit geringen LAI

Änderungen konnte die Wichtigkeit des Red-Edge Bandes herausgestellt werden. Die

Notwendigkeit multi-temporaler LAI-Analysen wurde verdeutlicht, weil der Blattflächen-

index über das ganze Jahr verschiedensten Umwelteinflüssen und phänologischen Ände-

rungen unterworfen ist.

Content

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Content

AUTHOR’S DECLARATION / EIDESSTATTLICHE ERKLÄRUNG ....................... 2

SUMMARY .......................................................................................................................... 3

ZUSAMMENFASSUNG ..................................................................................................... 4

LIST OF ABBREVIATIONS ............................................................................................. 7

LIST OF FIGURES AND TABLES .................................................................................. 8

I INTRODUCTION ........................................................................................................ 9

II JOURNAL ARTICLE ............................................................................................... 11

1. INTRODUCTION ................................................................................................ 13

2. MATERIAL AND METHODOLOGY .............................................................. 16

2. Study Site .............................................................................................................................. 16

2.2 Data Acquisition ................................................................................................................ 17

2.2.1 Sampling ................................................................................................................................... 17

2.2.2 LI-COR 2200 PCA Acquisition ................................................................................................ 18

2.2.3 RapidEye Acquisition ............................................................................................................... 19

2.3 Data Processing .................................................................................................................. 20

2.3.1 LI-COR 2200 PCA Processing .................................................................................................. 20

2.3.2 RapidEye Processing ................................................................................................................. 20

2.3.3 Comparing Temporal Variability of Field-based LAI and Satellite Derived SVI ..................... 23

Content

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3. RESULTS .............................................................................................................. 25

3.1 LAI Temporal Profile (LI-COR 2200) ............................................................................. 25

3.2 SVI Temporal Profiles (RapidEye) .................................................................................. 26

3.3 LAI-SVI Relationships ...................................................................................................... 28

3.3.1 In Phenological Phases .............................................................................................................. 28

3.3.2 Whole Vegetation Period .......................................................................................................... 29

4. DISCUSSION ....................................................................................................... 31

5. CONCLUSIONS .................................................................................................. 33

6. ACKNOWLEDGEMENTS ................................................................................. 33

III REFERENCES ........................................................................................................... 35

IV ACKNOWLEDGEMENTS ....................................................................................... 40

List of Abbreviations

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List of Abbreviations

DOY – day of year

GCP – ground control points

GPS – global positioning system

LAI – leaf area index

mSR-RE – modified red edge simple ratio

NDVI – normalized different vegetation index

NDVI-RE – red edge normalized different vegetation index

NIR – near infrared

PCA – LI-COR 2200 plant canopy analyzer

RE – red edge

RMSE – root mean square error

SVI – spectral vegetation indices

List of Figures and Tables

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List of Figures and Tables

Fig. 1. The research area, located in Mecklenburg-West Pomerania. The RapidEye scene

from May 22nd in 2011 (RGB – 321) shows the two forest sites Wendeforst and Uposter

Gehege with the 50 sample plots (yellow dots). .................................................................. 17

Fig. 2. Arrangement of the 5 sample points (yellow dots) of each plot, including the PCA

estimated area of measurement (grey solid lines), using the RapidEye scene from May

22nd in 2011 (RGB – 321). ................................................................................................. 18

Fig. 3. Idealized scheme of the main phenological periods of black alder, regarding to the

changes in SVI and field-based LAI. The data sets in the different periods are illustrated by

solid arrows showing the development of data with decreasing DOY................................ 24

Fig. 4. Seasonal mean LAI curve with standard deviation, divided into the three

phenolocical phases, measured from May until November 2011 using LI-COR 2200 PCA.

............................................................................................................................................. 25

Fig. 5. Seasonal mean curve of NDVI, NDVI-RE, mSR-RE, and curvature with standard

deviation, divided into the four phenological phases, measured from April until November

2011 (DOY 99-312) using RapidEye satellite data. ............................................................ 27

Fig. 6. Comparison of LAI and NDVI (1st column), NDVI-RE (2nd column), mSR-RE

(3rd column), and curvature (4th column), in the 4 phenological periods represented by the

4 lines, showing the coefficient of determination (R²) and the resulting equation (y = LAI,

x = SVI). .............................................................................................................................. 29

Fig. 7. LAI-SVI relationships of the vegetation period 2011, showing the coefficient of

determination (R²) represented through a red line. .............................................................. 30

Table 1. The spectral vegetation indices (NDVI, NDVI-RE, mSR-RE, curvature) with

equations, descriptions, and references used in the study. .................................................. 22

Annotation from the author:

All figures and tables with no reference are created within this study and belong to the re-

spective author. Figures taken from other sources are indicated and used to the best of

one’s knowledge.

I Introduction

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I Introduction

Worldwide different regulations exist to protect and conserve ecosystems and species. The

Convention on Biological Diversity (United Nations, 1992) for example defines that all

states shall conserve their biological diversity. This means the protection of the variability

within species, between species, and ecosystems (Art. 2). An important article to fulfill

these requirements is article 7, defining that biodiversity shall be monitored. The European

Union implements this article in the EU Habitat Directive 92/43/EEC (The Council of the

European Communities, 2007). It requires a six-year evaluation of the conservation status

for all Natura 2000 sites in Europe. Each EU member state has the duty to monitor special

species and habitat types, for example Alluvial forests with Alnus glutinosa and Fraxinus

excelsior (Alno-Padion, Alnion incanae, Salicion albae) (Art. 11 and Annex I, Nr. 91E0

Directive 92/43/EEC). The Scientific Working Group of the EU Commission suggests ex-

plicit methods for the monitoring. But for forest biotopes no suggestions exist, because of

missing methods and data. Due to that lack it is important to develop operational parameter

and indices. The leaf area index, as a key indicator for physical and biological processes

related to vegetation dynamics, can be used to meet this challenge. It is for example used to

characterize plant canopies, and to make a statement to biodiversity.

A forest ecosystem is very important, especially in terms of climate change. It is directly

related to species diversity, regulates hydrological flows, conserves soil, and can be de-

fined as carbon dioxide (CO2) sink. Due to dewatering intact alluvial forests with black

alder (Alnus glutinosa) become rare (Aas, 2003). Thus, adequate management strategies

are required to protect or restore these ecosystems. To do so multi-temporal monitoring is

needed.

Remote sensing offers less cost and labor intensive methods in comparison to ground

measurements to monitor the leaf area index in deciduous forests. It allows a larger spatial

and temporal sample to be obtained with minimum effort (Jonckheere et al., 2005).

With this master thesis I want to make a contribution to suggest forest monitoring methods

in alluvial forests based on remote sensing techniques, because of the above mentioned

reasons. To popularize the findings to other scientists the thesis is written as a scientific

article. It contains information about the study site and the used methods, describes and

I Introduction

10

discusses the results, and finally concludes all results. The submission to an adequate jour-

nal will be very soon.

II Journal Article

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II Journal Article

“Estimation of Seasonal Leaf Area Index in an Alluvial Forest Using High Resolution

Satellite-based Vegetation Indices”

Adina Tillack, Anne Clasen, Michael Förster, Birgit Kleinschmit

II Journal Article

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Estimation of Seasonal Leaf Area Index in an Alluvial Forest

Using High Resolution Satellite-based Vegetation Indices

Adina Tillacka, Anne Clasen

a, b, Michael Förster

a, Birgit Kleinschmit

a

aDepartment of Geoinformation in Environmental Planning,

Technical University of Berlin, 10623 Berlin, Germany

bHelmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany

Abstract

The leaf area index (LAI), as a key indicator for physical and biological processes related

to vegetation dynamics, is valuable to monitor the biomass of forests. Due to the

phenological development of trees it shows a high seasonal variability. This study estimat-

ed the LAI through field measurements and satellite derived spectral vegetation indices

(SVI) in two alluvial forest sites. The main objective of this paper was the validation of

seasonal relations between field measured LAI of the LI-COR 2200 plant canopy analyzer

(PCA) and four satellite derived spectral vegetation indices (SVI) of ten RapidEye images:

the normalized different vegetation index (NDVI), the red edge NDVI (NDVI-RE), the

modified red edge simple ratio (mSR-RE), and curvature. The indices were compared by

four phenological phases (leaf flushing until crown closure, leaf growing under crown clo-

sure, decreasing leaf chlorophyll content, leaf senescence), and over the whole vegetation

period in 2011 using regression analyzes. The results suggest that LAI-SVI relationships

varied seasonally. Strong to weak linear relationships were obtained in the different peri-

ods. For each phase a different SVI fitted best: NDVI-RE in the period of leaf flushing

until crown closure (R² = 0.62), mSR-RE in the phases of leaf growing under crown clo-

sure (R² = 0.422), NDVI-RE in the period of decreasing leaf chlorophyll content (R² =

0.182), and NDVI during leaf senescence (R² = 0.829). Thus, implementing the red edge

channel of RapidEye data, improved LAI-SVI relations especially in periods with less de-

or increase of LAI. When analyzing the whole vegetation period, NDVI had the best re-

gression (R² = 0.942) because it is the most stable index due to moderate LAI values (aver-

age max. LAI = 4.63). The used satellite-based vegetation indices provided reliable esti-

mates and described temporal changes and spatial variability of LAI well. In addition,

when monitoring alluvial forest dynamics, the multi-temporal approach is recommended.

II – Introduction

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Keywords: alluvial forest; Alnus glutinosa; curvature; leaf area index; modified red edge

simple ratio; multi-temporal; normalized different vegetation index; phenology; RapidEye;

red edge normalized different vegetation index; spectral vegetation indices

1. Introduction

Regular and accurate monitoring of forests enables the detection of forest damages, caused

for example by nitrogen deficiencies, water stress or insect diseases, helping to develop

adequate management strategies. Especially in terms of climate change the effect of forest

stress might be intensified (Eitel et al., 2011).

There is an immediate need for recommendations to monitor spatial-temporal dynamics of

forest biotopes, because of missing methods and data. One key feature for monitoring for-

est ecosystems is the canopy structure. It influences and is influenced by processes of the

ecosystem, such as temperature, moisture or net primary productivity and changes within

minutes, seasons, and years (Weiss et al., 2004). An important characteristic of the canopy

structure is the leaf area index (LAI) (Chason et al., 1991). This index is a key indicator for

physical and biological processes related to vegetation dynamics at global and regional

scale, for example energy exchange, water, and carbon cycle (Fassnacht et al., 1994; Chen

et al., 2002). LAI was first defined after Watson (1947) as a dimensionless quantity, the

total one-sited area of photosynthetic tissue per unit soil surface area. That means the LAI

is a quantity for the potential area of a habitat type which is photosynthetically active. For

temperate deciduous forests, the phenology determines the length of the growing season.

Therefore the seasonal course of the LAI represents the phenological phenomena (Wang et

al., 2005b).

LAI can be derived by direct measurements and indirect estimates. Direct methods involve

destructive sampling of leaves, litterfall collection, or point contact sampling (e.g. Chason

et al., 1991; Wang et al., 2005a). For indirect measurements optical instruments, models,

and remote sensing data are used (e.g. Dufrêne & Bréda, 1995; Bréda, 2003; Wang et al.,

2005a). Direct estimates provide the closest values to true LAI, but they are very time-

consuming, labor intensive, and destructive, which is undesirable especially in protected

forests (Fassnacht et al., 1994; Eschenbach & Kappen, 1996). Thus, there is a great interest

II – Introduction

14

in using remote sensing data to estimate the LAI. This method is particularly suitable for

the monitoring of vegetation, allowing a larger spatial and temporal sample to be obtained

with minimum effort (Jonckheere et al., 2005).

Since the 1970s it is known that LAI is strongly related to spectral measurements, because

it influences the reflectance characteristics of vegetation (Tucker, 1979; Chen et al., 2005).

Thus, many agricultural and forest (conifer and deciduous stands) studies have successfully

linked the LAI to remotely sensed data (e.g. Chen & Black, 1991; Chen & Cihlar, 1996;

Colombo et al., 2003; Brantley et al., 2011). For deriving LAI a priori functions can be

used, where field measurements are related to remotely sensed data (Chen & Cihlar, 1996;

Turner et al., 1999; Chen et al., 2005). The logarithmic relationships between LAI and

combinations of spectral vegetation indices, named SVI, were proven and analyzed in

many studies (e.g. Tucker, 1979; Myneni et al., 1995; Datt, 1999; Mutanga & Skidmore,

2004). When computing SVI green leaf characteristics were utilized. Although they are

species dependent they show similar features. In the visible light (0.4-0.7 µm) the reflec-

tion is low, because of the maximum chlorophyll absorption, especially at 0.69 µm (red

wavelength). In the near-infrared region (NIR, 0.7-1.3 µm) leaves reflect the solar radia-

tion very well. This is caused by internal mesophyll cellular structure and leaf surface scat-

tering (Myneni et al., 1995; Chen et al., 2005). Many SVI were suggested to reduce un-

wanted environmental noise in satellite data caused for example by solar geometry, uneven

atmospheric conditions, topography, and secondary canopy effects. Thus, they enhance the

responsivity to canopy characteristic like LAI (Chen et al., 2005; Wu et al., 2007). For this

reason most spectral vegetation indices combine red/near-infrared bands to utilize leaf at-

tributes (Reed et al., 2003). So these indices are useful for forest monitoring because dam-

ages affect the absorption of photosynthetically active radiation induced by the loss of

chlorophyll (Eitel et al., 2011).

Most studies mentioned above used satellite systems (e.g. Landsat TM, NOAA AVHRR,

SPOT, Terra Modis) with low to medium spatial resolution (> 30 m) and did not explore

the full temporal resolution to assess the LAI from remote sensing, (e.g. Chen et al., 2002;

Wang et al., 2005a). However, because of the heterogeneity of the earth's surface, these

products have large uncertainties and their use is limited for this purpose (Chen et al.,

2002; Eitel et al., 2007). For this reason SVI were investigated with very high spatial reso-

lution satellite products in recent studies. Ikonos with a spatial resolution of 4 m,

II – Introduction

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QuickBird with a spatial resolution of 2.8 m, and RapidEye data with a spatial resolution

of 6.5 m were used for estimating the LAI of different agricultural crops and grassland

(Colombo et al., 2003; Wu et al., 2007).

LAI estimations depend on species composition, development stage, stand conditions, sea-

sonality, and used management practices (Jonckheere et al., 2004). Changing phenology as

well as environmental conditions affect the seasonal LAI course in various development

stages of mid-latitude vegetation. Multi-temporal analysis of surface reflectance is neces-

sary to examine the changes in the vegetation surface. Even though this is an important

aspect, it has been neglected in most of the mono-temporal studies, although it is crucial

for the transfer of the outcomes to other acquisition dates. Most previous studies estimated

the LAI using data of only one development stage (mostly June to August) (e.g. Chen,

1996; Chen et al., 2002; Colombo et al., 2003). Only a few research projects like Kodani et

al. (2002), Wang et al. (2005a), Eitel et al. (2011), and Conrad et al. (2012) examined mul-

ti-temporal LAI estimations, including different phenological stages.

The research of alluvial deciduous forests was only done by Eschenbach & Kappen (1996)

who derived the LAI of Alnus glutinosa, but without using satellite products. Thus, in this

study the LAI dynamics of black alder (Alnus glutinosa) were investigated as an example

of alluvial forest species. The LAI field measurements were related to a time-series of ten

satellite images of one vegetation period by using four spectral vegetation indices: the

normalized difference vegetation index (NDVI) (Rouse et al., 1973; Tucker, 1979), the red

edge NDVI (NDVI-RE) (Gitelson & Merzlyak, 1994; Sims & Gamon, 2002), the modified

red edge simple ratio (mSR-RE) (Datt, 1999; Sims & Gamon, 2002), and curvature

(Conrad et al., 2012). In situ LAI-time series retrieved from the optical instrument LI-COR

2200 plant canopy analyzer (PCA) served as reference data. RapidEye products were used

as high resolution satellite images. It is the first satellite system providing the red edge

spectrum operationally (Schuster et al., 2012). Therefore RapidEye serves as a new source

for monitoring stand specific productivity over a certain time interval.

II – Material and Methodology

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The main objectives of this paper are:

1) The validation of four spectral vegetation indices of RapidEye products to estimate

seasonal LAI values of alluvial forests,

2) A comparison of LI-COR 2200 PCA measured and RapidEye derived SVI,

3) The exposure of possible advantages of multi-date LAI analysis over the whole

vegetation period in comparison to LAI values of one phenological phase.

2. Material and Methodology

2.1 Study Site

Data were collected during the vegetation period of 2011 in two alluvial forests around

Demmin (8 m above sea level) in Mecklenburg-West Pomerania, in the northeast of Ger-

many. The research sites were Uposter Gehege (53°53’N and 12°58’E), and Wendeforst

(53°55’N and 12°57’E) (see Fig. 1). Both forests are part of or close to nature protection

areas. The climate zone is moderate, with an average annual temperature of 8-8,5 °C and

an average annual rainfall of 550-600 mm.

II – Material and Methodology

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Fig. 1. The research area, located in Mecklenburg-West Pomerania. The RapidEye scene from May 22nd in 2011 (RGB –

321) shows the two forest sites Wendeforst and Uposter Gehege with the 50 sample plots (yellow dots).

The dominant tree species at the study site is black alder (Alnus glutinosa) which covers

more than 90% of the investigated forest stands. It is typical for northern Germany, espe-

cially in the given floodplain area on soils permanently waterlogged in winter and tempo-

rally dewatered in summer. Other tree species at the site include Salix caprea, Betula

pendula, Fraxinus excelsior, Picea abies, and Fagus sylvatica. Understory vegetation con-

sisted of common species depending to the stand, mainly Corylus avellana, Iris

pseudacorus, Urtica dioica, and Carex acutiformis. The ages of the trees reached up to 80

years with a tree height of 15-28 m, but most trees were 41-80 years old with an average

height of 21.5 m.

2.2 Data Acquisition

2.2.1 Sampling

It was possible to locate and use 12 test sites where Alnus glutinosa is the dominant tree

species. These sites were chosen on the basis of orthophoto interpretation, silvicultural

data, and field exploration. Tree height, view angle (58.1° in ring 4), the 90° view caps,

and the north-direction of the LI-COR-2200 PCA were consulted to avoid overlaps of the

II – Material and Methodology

18

sample plots. 50 sample plots were installed in total (see Fig. 1). Each plot includes 5 sam-

ple points: one in the middle and around this, with a distance of 5 m, 4 points in every geo-

graphic direction (see Fig. 2).

Fig. 2. Arrangement of the 5 sample points (yellow dots) of each plot, including the PCA estimated area of measurement

(grey solid lines), using the RapidEye scene from May 22nd in 2011 (RGB – 321).

2.2.2 LI-COR 2200 PCA Acquisition

LAI field data were collected every two or three weeks (depending on weather conditions)

from the beginning of May until the middle of November during the vegetation period of

2011 (Day of year, DOY: 122, 139, 158, 179, 200, 241, 263, 284, and 312). The field

measurement took place nearly at the same dates the RapidEye data were taken (at max. 6

days apart). The LAI-2200TC plant canopy analyzer package was used. It measures diffuse

light (320-490 nm) under the canopy with a „fish-eye“ optical sensor, and calculates the

gap fraction, the fraction of view from beneath a canopy that is not blocked by foliage

(transmittance at ground level), for five concentric rings (with central zenith angle of 7°,

23°, 38°, 53°, 68°) (LI-COR Inc., 2010). More information about the theoretical back-

ground of the gap fraction is given in Weiss et al. (2004). According to Eriksson et al.

(2005) and Eschenbach & Kappen (1996) the PCA is a satisfactory technique for measur-

ing the LAI of black alder due to moderate LAI values between 2 and 5, and a relatively

homogeneous foliage distribution in the crown periphery. Data acquisition was done by

two optical sensors and one control unit. One sensor recorded the below canopy readings

II – Material and Methodology

19

and the other was placed in open fields nearby the forest to measure the open sky simulta-

neously. The LAI was estimated by inversion of the Poisson model comparing the trans-

mittances of the two sensors (Jonckheere et al., 2004; LI-COR Inc., 2010).

Measurements were mostly carried out at an average of two hours before sunset and after

sundown during clear sky, and according to the weather conditions under uniform overcast

sky to reduce the effect of scattered light in the canopy. To get comparable results it is im-

portant that both sensors measure under the same conditions. Therefore the sensors were

oriented towards north, held level at arms’ length, at hip height, and 90° view caps were

used which covered 75% of the sensors.

2.2.3 RapidEye Acquisition

For the satellite data the commercial multispectral earth observation mission RapidEye

comes into operation, which consists of five micro-satellites. The sensors provide products

since February 2009 using five spectral bands with wavelength between 440-850 nm, in-

cluding the red edge band (Band 1: 440-510 nm, Band 2: 520-590 nm, Band 3: 630-685

nm, Band 4: 690-730 nm, Band 5: 760-850 nm) (Sandau, 2010). The red edge channel

marks the change between chlorophyll absorption in the red and leaf scattering in the near-

infrared wavelength (Curran et al., 1990). Earlier studies on the theoretical background of

the red edge band used field spectrometry and image spectroscopy of airborne images.

These showed a strong correlation between chlorophyll concentration and the red edge

spectral reflectance of vegetation, which is the point of maximum slope (e.g. Curran et al.,

1990; Filella & Penuelas, 1994; Pinar & Curran, 1996; Sims & Gamon, 2002; Hansen &

Schjoerring, 2003; Potter et al., 2012).

A total of 20 RapidEye satellite images (10 for each research site) were acquired at level

3A with a pixel size of 5 m and a geometric accuracy of 14 m (root mean square error,

RSME = 6 m), between 9th of April and 8th of November 2011 (DOY: 99, 110, 116, 125,

142, 157, 197, 267, 290, 312). The images were radiometrically, sensor, and geometrically

corrected by RapidEye AG, aligned to a cartographic map projection and nearly cloud-free

(~90 %) (Rapideye AG & RapidEye US, LLC, 2011).

II – Material and Methodology

20

2.3 Data Processing

2.3.1 LI-COR 2200 PCA Processing

For computing LAI the software FV2200 was used (LI-COR Inc., 2010). In a first step,

the measurements from the below and above canopy sensor were matched using the closest

readings in time. To reduce the known underestimation of the LAI in comparison to real

LAI (not plant area index or vegetation area index), detector ring 5 (61-74°) was omitted

(e.g. Chason et al., 1991; Dufrêne & Bréda, 1995; Cutini et al., 1998). The underestimation

of LI-COR 2200 LAI increase with higher zenith angle (Jonckheere et al., 2004). For the

computation of LAI the horizontal model of FV2200 is used. All rings of the sensor face

the top of the wide and flat canopy, like it is assumed for ideal forests. To consider this

assumption each sample point included alder trees of the same age only, and therewith of

the same height, although there appeared different tree ages at one site.

2.3.2 RapidEye Processing

RapidEye data were geocorrected manually by georeferencing all images to an

orthorectified reference image with a ground resolution of 0.4 m, resulting in a final accu-

racy of about one pixel. In addition all images were atmospherically corrected using the

parametric correction tool ATCOR to reduce atmospheric effects (Richter, 1996). The cor-

rections were made scene by scene using specific atmospheric conditions depending on the

image acquisition date.

The following four SVI were calculated (see Table 1):

1) normalized difference vegetation index (NDVI),

2) red edge normalized difference vegetation index (NDVI-RE),

3) modified red edge simple ratio (mSR-RE), and

4) curvature.

NDVI was used because it is the most utilized index related to LAI (Chen & Cihlar, 1996;

Berterretche et al., 2005). It has proven to be robust over a wide range of conditions, and

reduces atmospheric and illumination influences. The other indices were chosen due to

their different band combinations using red or blue wavelength (0.44-0.51 µm) but includ-

ing the red edge and near infrared (NIR) band. Especially the red edge channel is sensitive

to small changes in the canopy, gap fraction, and senescence (Potter et al., 2012). At high

II – Material and Methodology

21

chlorophyll content the leaf reflection in the red edge decreases, showing the shift to higher

wavelength (Horler et al., 1983). Sims and Gamon (2002) developed the modified red edge

simple ratio index, including the blue band to eliminate the effect of leaf surface reflec-

tance. According to Sims and Gamon (2002) this index is better correlated to chlorophyll

content than the original simple ratio index. Curvature was developed by Conrad et al.

(2012) because different hyperspectral studies showed that the spectral curve behavior

(slope of red edge) between red and NIR correlates with the chlorophyll content of vegeta-

tion.

The pixels to compute the selected SVI, were chosen by positioning the measured LAI-

2200 area (depending on tree height) through GPS points in the georeferenced and atmos-

pherically corrected RapidEye images. All pixels situated in the estimated area of meas-

urement of the PCA were analyzed.

II – Material and Methodology

22

Table 1

The spectral vegetation indices (NDVI, NDVI-RE, mSR-RE, Curvature) with equations, descriptions and references used in the study.

Vegetation Index

Equation

Value range of

green vegetation

Description

Reference

Normalized Difference

Vegetation Index

(NDVI)

0.2 – 0.8

Normalized difference of green leaf scattering in

NIR, chlorophyll absorption in Red.

Rouse et al., 1973; Tucker, 1979

Red Edge Normalized

Difference Vegetation Index

(NDVI-RE)

-

0.2 – 0.9 A modification of the NDVI using reflectance

measurements along the red edge.

Gitelson & Merzylak, 1994; Sims &

Gamon, 2002

Modified Red Edge

Simple Ratio

(mSR-RE)

-

2 – 8 Modification of the Simple Ration Vegetation

Index (ratio of green leaf scattering in NIR,

chlorophyll absorption in Red), using a ratio of

reflectance along the red edge with Blue reflec-

tion correction.

Datt 1999; Sims & Gamon, 2002

Curvature

-0.2 – 0.2 Curvature between Red and NIR, defined by the

position of the red edge, using reflectance meas-

urements and wavelength along Red, NIR and

red edge. It is described through the second

derivation.

Conrad et al., 2012

II – Material and Methodology

23

2.3.3 Comparing Temporal Variability of Field-based LAI and Satellite De-

rived SVI

When analyzing outcomes of the field-based LAI and the satellite derived SVI multi-

temporally, it is possible to investigate the whole vegetation period on the one hand. On

the other hand different development stages in a season can be distinguished (e.g. Wang, et

al. 2005a; Zhang et al., 2003; Kodani et al., 2002). Therefore the indices were compared in

two ways to identify which SVI fits best to the reference data:

1) by phenological phases, and

2) over the whole vegetation period.

The LAI-SVI relationships were investigated using the coefficient of determination (R²)

computed by the free software environment R-Statistics (R Foundation for Statistical

Computing, Vienna, Austria). For the phenological phases empirically derived linear re-

gression models came into operation, and for comparing LAI and SVI over the whole veg-

etation period third order polynoms were computed for each index and for all combined

acquisition dates.

The following three main periods can be separated when analyzing the phenology of black

alder measured by PCA and RapidEye data (Wang et al., 2005; Zhang et al., 2003; Kodani

et al., 2002):

1) leaf production period,

2) period of stable leaf area, and

3) period of leaf senescence.

Although the stages are the same they differ due to the measuring systems. Therefore four

phenological periods were defined when comparing LAI and SVI. Period of:

1) leaf flushing until crown closure,

2) leaf growing under crown closure,

3) decreasing leaf chlorophyll content, and

4) leaf senescence.

These stages are exemplary shown in Fig. 3. The onset of leaf growing starts later when

using LAI-2200 instead of analyzing satellite data. That is why understory vegetation de-

II – Material and Methodology

24

velops prior to leaves. The second stage was defined because satellite data are less sensi-

tive to leave growing after crown closure than PCA. When the maximum LAI is reached a

period of constant leaf area starts until the onset of senescence. During that phase the de-

scent of chlorophyll content starts. In comparison to other species, which are coloring yel-

low, leaves of Alnus glutinosa just dry out at the shoot and fall off (Pietzarka & Roloff,

2006). But only satellite data are sensitive to that leave reflection change. In comparison to

satellite measurements the LAI-2200 can only distinguish between ‘leaves’ or ‘no leaves’.

Fig. 3. Idealized scheme of the main phenological periods of black alder, regarding to the changes in SVI and field-based

LAI. The data sets in the different periods are illustrated by solid arrows showing the development of data with decreas-

ing DOY.

The coefficient of determination (R²) can be influenced by outliers (Fahrmeir et al., 2009;

Cohen et al., 2003) due to incorrect calibrated LI-COR sensors, inappropriate weather con-

ditions for the PCA, and geometric as well as atmospheric distortions of the RapidEye sen-

sors. Therefore the Bonferroni outlier test was used to identify the outliers within the as-

sumed 5% of all collected data. 5 % were chosen because outliers in a boxplot lie 2.5 %

above or below the whisker (Fahrmeir et al., 2009). The outliers were removed from the

following data sets: LAI and SVI plotted over DOY, and comparison of LAI and SVI.

II – Results

25

3. Results

3.1 LAI Temporal Profile (LI-COR 2200)

LI-COR LAI was computed for each measured above and below pair. Fig. 4 shows the

seasonal average LAI curve from LI-COR PCA with the standard deviation of all sample

plots in the study area during the growing season (DOY 122-312). Due to different

environmental conditions at the measuring sites (e.g. elevation, flooding), stand age, crown

closure, health status, and understory vegetation of the black alder trees the standard

deviation increases with increasing development stage and is lower in the leafless period.

For LI-COR LAI the three development stages can be distinguished as follows:

1) Period of leaf flushing until maximum LAI is reached: The flushing of leaves

started about DOY 115. The LAI increased almost linear from 1.59 to 4.63;

2) Period of stable leaf area: LAI was slightly decreasing from 4.63 to 3.94. This

development stage is special about Alnus glutinosa which differs from other

European deciduous tree species. New leaves are continuous growing in the

periphery during the growing season, whilst in summer litterfall starts in the inner

part of the crown (Pietzarka & Roloff, 2006; Eschenbach & Kappen, 1996);

3) Period of leaf senescence: The LAI decreased continuously from 3.94 to 0.95,

where all leaves had gone.

Fig. 4. Seasonal mean LAI curve with standard deviation, divided into the three phenolocical phases, measured from May

until November 2011 using LI-COR 2200 PCA.

II – Results

26

3.2 SVI Temporal Profiles (RapidEye)

In this section the four SVI curves NDVI, NVDI-RE, mSR-RE, and curvature are de-

scribed through the four development stages in deciduous forests (see section 0). Their

seasonal course is shown in Fig. 5.

The same phenomena as shown in the LAI temporal profile (Fig. 4) are visible: a bell

shaped curve. Nevertheless the onset day starts earlier, because the understory vegetation

develops prior to canopy development like in most European deciduous forest stands. The

values increased from April on until they reached a maximum around June/July (NDVI:

0.851, NDVI-RE: 0.598, mSR-RE: 5.359, Curvature: 0.121), and afterwards generally de-

creased until November. Details in the phenological development will be explained.

The following phenological development phases can be distinguished:

1) Period of leaf flushing until crown closure: At the end of March/ beginning of April

leaves of the understory vegetation began do develop. Around DOY 115 leaves of

black alder started to overlay underlying vegetation. It is assumed that this caused a

small change in all indices between DOY 110 and 125. But generally all SVI

increased from DOY 99 to DOY 157;

2) Period of leaf growing under crown closure: Between DOY 157 and 197 the NDVI

was stable around 0.85, because the red and NIR channel are not sensitive to leaf

growing under crown closure. The other indices still increased to the maximum

values, but with fewer slopes due to the sensitivity of the red edge band for small

changes;

3) Period of decreasing leaf chlorophyll content: The values of all four indices

decreased from DOY 197 to 267. It is noticeable that the red, red edge, and NIR

band are sensitive for the diminishing chlorophyll content of leaves;

4) Period of leaf senescence: NDVI and NDVI-RE showed the decreasing LAI very

well (see Fig. 4), due to leaf fall between DOY 267 and 312. The other two indices

were not qualified for describing the LAI in this period. Values of the mSR-RE

were out of range in this period. DOY 290 had too high values for October, and in

November (DOY 312) the index went below the limit of 2 for green vegetation.

This suggests that the used blue band, sensitive for water and moisture (water

reflects the blue wavelength), is responsible for the wrong values of mSR-RE.

II – Results

27

Especially in fall the atmospheric humidity, and resulting condensation water on

leaves is very high, which could have caused this effect. Curvature decreased in the

first part of the period (DOY 267-290), but in the second part it increased (DOY

290-312), in contrary to the diminishing LAI. Due to positive Curvature values the

red edge tended to turn left, which is typical for woody vegetation according to

Conrad et al. (2012).

Again because of the different environmental conditions as stated in chapter 3.1 the stand-

ard deviation is higher in the leaf flushing and senescence period, while it is lower in the

leafless and leaf constant period. The latter can be explained by the closed and full devel-

oped crown closure which is very similar in the different black alder stands when analyz-

ing satellite data.

Fig. 5. Seasonal mean curve of NDVI, NDVI-RE, mSR-RE, and curvature with standard deviation, divided into the four

phenological phases, measured from April until November 2011 (DOY 99-312) using RapidEye satellite data.

II – Results

28

3.3 LAI-SVI Relationships

3.3.1 In Phenological Phases

The results of this analysis are presented in the form of R² und the resulting equation based

on linear regression for each LAI and SVI pair, in Fig. 6. In general it could be determined

that the 4 phenological phases of Alnus glutinosa differed among each other due to their

LAI-SVI relationships. In period 1 (DOY 125-157) and 4 (DOY 267-312) these were rela-

tively good, whereas in period 2 (DOY 157-197) and 3 (DOY 197-267) they were moder-

ate to low, with partly no clear relation between LAI and SVI. One reason for the lower

relations was the examination of only two points in time instead of three in period 1 and 4.

Another reason was that field LAI only included overstory vegetation while the SVI in-

cluded overstory and understory vegetation. Nevertheless it turned out that for each phase a

different SVI fitted best.

In the period of leaf flushing until crown closure all indices possessed similar R² with

moderate to good positive linear relations. LAI-NDVI-RE had the best relationship with

R² = 0.62, showing that the coefficient of determination was higher when the red edge

channel is included:

LAI = -2 + 8.9 ∙ NDVI-RE (1).

In comparison to the period of leaf growing under crown closure one could see lower R².

Especially the NDVI showed no relation to LAI, because it saturated at LAI ≥ 5. So the

results indicated poor performance of NIR-red NDVI as compared to a red edge SVI com-

bination. Thus, the mSR-RE, using the blue, red edge, and NIR band, had the best result

(R² = 0.422):

LAI = -1.2 + 1.1 ∙ mSR-RE (2).

The linear regression of the third period with decreasing chlorophyll content showed a

marginal relationship of the different SVI to LAI. NDVI and NDVI-RE were slightly better

as mSR-RE and curvature, while NDVI-RE had the best coefficient of determination with

R² = 0.182:

LAI = 0.29 + 7.2 ∙ NDVI-RE (3).

The field measured LAI was still constant, or rather decreased a bit, because the leaves

dried out. Due to the diminishing chlorophyll content the different indices showed a

II – Results

29

stronger decrease. The last phase of leaf senescence differed considerably from the others.

R² discriminates between the indices: Curvature had the lowest relation (R² = 0.118), and

NDVI fitted best to the LAI values with R² = 0.829:

LAI = -2 + 7.3 ∙ NDVI (4).

Fig. 6. Comparison of LAI and NDVI (1st column), NDVI-RE (2nd column), mSR-RE (3rd column), and curvature (4th

column), in the 4 phenological periods represented by the 4 lines, showing the coefficient of determination (R²) and the

resulting equation (y = LAI, x = SVI).

3.3.2 Whole Vegetation Period

Fig. 7 shows the results of the LAI-SVI relationships over the whole vegetation period in

2011. The third order polynoms for each index over the time were compared to the third

II – Results

30

order polynom of the field measured LAI by computing R², which is represented through a

red line. Additionally the red line showed the development of the LAI-SVI relations over

the time (from DOY 125 to DOY 312). Here R² was much higher compared to the

phenological periods, because of larger examined ranges of values, including the same

peak-to-valley values. It turned out that the NDVI fitted best (R² = 0.942) before NDVI-

RE, curvature, and mSR-RE, although the index only dominated one of the four

phenological phases. When comparing NDVI to the other indices, reasons for the good

coefficient of determination could be the less scatter of data, no values out of range, and a

similar development of both data sets (LAI and NDVI), except in the period of leaf grow-

ing under crown closure. The effect of saturation, occurring only at DOY 197, had hardly

any influence on R² because of the 3rd

order polynom. Especially the values in the period of

leaf senescence (DOY 290 and 312) from mSR-Re and curvature were out of character,

which caused lower R².

Fig. 7. LAI-SVI relationships of the vegetation period 2011, showing the coefficient of determination (R²) represented

through a red line.

II – Discussion

31

4. Discussion

The results clearly revealed the seasonal variations of LAI and SVI depending on alluvial

forest phenology of Alnus glutinosa. The analysis showed different relationships between

field measured LAI and satellite derived NDVI, NDVI-RE, mSR-RE, and curvature. Di-

verse factors affect these relations: e.g. species composition, stand age, crown closure,

woody elements, and background reflectance (Colombo et al., 2003). It could be empha-

sized that different SVI should be used for estimating LAI, corresponding to the four

phenological periods (leaf flushing until crown closure , leaf growing under crown closure,

decreasing leaf chlorophyll content, leaf senescence). This result agrees with the one of

Wang et al. (2005a), suggesting the use of different NDVI-LAI relationships depending to

three phenological phases. When analyzing the four periods the red edge channel provided

better relations to LAI, as not incorporating this band (NDVI). Especially in terms of less

LAI decrease or increase the red edge is sensitive to small changes in spectral reflectance.

These findings can be confirmed by Filella & Penuelas (1994), Eitel et al. (2011), and

Schuster et al. (2012). Therefore the NDVI-RE and the mSR-RE, including the red edge

channel, were the best indices in three of four periods. However, concerning the mSR-RE,

the findings of Sims & Gamon (2002), showing this index correlated better in the leaf se-

nescence period than NDVI, could not be confirmed. NDVI was weak especially during

the period of leaf growing under crown closure, but showed strong linear relationships in

the period of leaf senescence, which agrees with the findings of Eitel et al. (2011), who

found the NDVI more indicative to stress related changes than NDVI-RE in the needle loss

season. Chen & Cihlar (1996) also found the sensitivity of NDVI better in late spring as in

mid-summer. This can be explained by the saturation of NDVI at LAI ≥ 5 which is espe-

cially a problem in the period of leaf growing under crown closure, when the foliage is

quite dense (e.g. Myneni et al., 1995; Turner et al., 1999; Sims & Gamon, 2002; Chen et

al., 2005). NDVI is insensitive to changes in chlorophyll for moderate to high content

(Eitel et al., 2011). Including the red edge channel can help to solve this problem like pre-

vious studies had already proven (e.g. Eitel et al., 2011; Potter et al., 2012; Schuster et al.,

2012). Eitel et al. (2011) also stated that stress induces changes of chlorophyll in forests

correlated most with NDVI-RE followed by NDVI. The red edge band is more sensitive to

stress because of stress induced increase in flourescene. Curvature indicated an improve-

ment to NDVI in the first two phases, but had not the expected potential when estimating

II – Discussion

32

LAI in alluvial forests in comparison to crops used by Conrad et al. (2012). Nevertheless,

when looking at the whole vegetation period the red edge channel had no advantage in

relation to LAI. NDVI exhibited the strongest regression, because it is the most stable in-

dex. This is due to moderate LAI values in black alder stands. So the effect of saturation,

occurring only at DOY 197, can be neglected. The strong NDVI-LAI relation can be con-

firmed by Wang et al. (2005a) (R² = 0.99 for 1996). In addition studies like Kodani et al.

(2002) and Wang et al. (2005a) made time series trajectories of NDVI and LAI, which are

similar to ours. The average maximum LAI (4.63) is alike the one of Eschenbach &

Kappen (1996) (approx. 4.5 in July). As RapidEye images of DOY 241 (august) had to be

omitted, it can be assumed that the average maximum LAI was reached in august, like stat-

ed by Eschenbach & Kappen (1996).

As changing phenology, as well as environmental conditions affect the seasonal LAI

course, multi-temporal estimations are very important. Especially for monitoring, where

different years were compared. The range (from lowest to maximum LAI), and the mini-

mum and maximum LAI values are needed to derive long term trends, which are for ex-

ample important when looking at the influence of climate change. Precisely the starting

and ending values can give information about woody elements (before leaf flushing) and

understory vegetation (beginning of leaf flushing, before the development of canopy). In

addition short term occurrences like insect diseases, water stress or storm loss, affecting

the LAI, can be identified in any period. Due to that adequate management strategies could

be implemented very fast. Therefore, seasonal pattern of different SVI are useful for esti-

mating seasonal LAI course.

Further research is needed to assess the transferability of the used LAI-SVI relationships

among alluvial forest sites quantified by RapidEye data. Especially the influence of stand

ages, health status, and understory vegetation should be investigated. So we recommend

similar studies to be conducted in other alluvial forest regions, for instance in Mediterrane-

an or boreal (subarctic) climates. In a second step the relations could be estimated by using

other high resolution satellite data, since more and more new satellite systems will be de-

veloped with a better spectral resolution. Additionally, the findings of this study should be

compared to other forest species and to other methods of measurement (e.g. hemispherical

photographs).

II – Conclusions / Acknowledgements

33

5. Conclusions

The paper has investigated LAI-SVI relationships from April to November in 2011 for an

alluvial forest with black alder. NDVI, NDVI-RE, mSR-RE, and curvature were derived

from RapidEye satellite data, while field LAI was measured by LI-COR 2200 PCA.

It was obtained that LAI-SVI relationships varied seasonally in the four phenological phas-

es. Strong relationships occurred during the periods of leaf flushing until crown closure

and leaf senescence. While in the periods of leaf growing under crown closure and de-

creasing leaf chlorophyll content the relations were weaker. In four phenological phases

different SVI dominated:

NDVI-RE in the period of leaf flushing until crown closure:

LAI = -2 + 8.9 ∙ NDVI-RE (1),

mSR-RE in the period of leaf growing under crown closure:

LAI = -1.2 + 1.1 ∙ mSR-RE (2),

NDVI-RE in the period of decreasing leaf chlorophyll content:

LAI = 0.29 + 7.2 ∙ NDVI-RE (3),

and NDVI in the period of leaf senescence:

LAI = -2 + 7.3 ∙ NDVI (4).

This shows the importance of incorporating the red edge channel especially in terms of less

LAI decrease or increase. In addition NDVI had the best relation over the whole vegetation

period due to moderate LAI values. The study indicated that multi-temporal estimations of

LAI are very important, because of changing phenology and different environmental influ-

ences throughout the year.

6. Acknowledgements

We greatly appreciate the help of Tino Bertram, Oscar Zarzo Fuertes, Lisa Heinsch, Tobias

Schmidt, Nadja Tegtmeyer, and Björn Welle with the field measurements and/ or pro-

cessing of RapidEye data. The study was connected to the research project „ForstHype“.

This is funded by the German National Space Agency DLR (Deutsches Zentrum für Luft-

II – Acknowledgements

34

und Raumfahrt e.V.) on behalf of the German Federal Ministry of Economy and Technol-

ogy based on the Bundestag resolution 50EE1025. We acknowledge the DLR for the de-

livery of RapidEye images as part of the RapidEye Science Archive – proposal 469. We

also thank the TERENO (Terrestrial environmental Observatories) Northeastern Germany

Lowland Observatory for enabling the intensive field campaign.

III References

35

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IV Acknowledgements

40

IV Acknowledgements

I want to thank Prof. Dr. Birgit Kleinschmit for offering me the possibility to write this

master thesis within the research project “ForestHype” as a journal article. Additionally I

greatly appreciated the help of Anne Clasen and Micheal Förster for supporting me for

example in the application of software, and text work. Many thanks go also to colleagues,

tutors and my boyfriend who answered my questions, helped with the field work and/ or

processing of data, especially with R-Statistics. The experiences I have made during the

field and office work were enriching, interesting and confirmed my resolution to continue

working in the field of remote sensing.