Ecosystem health assessment based on remote sensing a case study of ca river basin, vietnam

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ECOSYSTEM HEALTH ASSESSMENT
BASED ON REMOTE SENSING
A CASE STUDY OF CA RIVER BASIN, VIETNAM
Bao Quoc Tran
MSc Thesis WM-WRM.16-22
Student number: 47074
April 2016


ECOSYSTEM HEALTH ASSESSMENT BASED ON
REMOTE SENSING
A CASE STUDY OF CA RIVER BASIN, VIETNAM

Master of Science Thesis
by

Bao Quoc Tran

Supervisors
Prof. Wim G.M. Bastiaanssen

Mentors
Hans van der Kwast, PhD
Tim Hessels, MSc
Examination committee
Prof. Wim G.M. Bastiaanssen
Hans van der Kwast, PhD
Tim Hessels, MSc
Ir. G.J. Roerink (WUR-Alterra)

This research is done for the partial fulfilment of requirements for the Master of Science degree at the
UNESCO-IHE Institute for Water Education, Delft, the Netherlands

Delft
April 2016


Although the author and UNESCO-IHE Institute for Water Education have made every effort
to ensure that the information in this thesis was correct at press time, the author and UNESCOIHE do not assume and hereby diSLCaim any liability to any party for any loss, damage, or
disruption caused by errors or omissions, whether such errors or omissions result from
negligence, accident, or any other cause.
© Bao Quoc Tran 2016.
This work is licensed under a Creative Commons Attribution-Non Commercial 4.0
International License.


Abstract
The Ca river basin is the third biggest river basin in Vietnam, in which the upper part
belongs to Laos. With the World Biosphere Reserve Western Nghe An officially recognized by
UNESCO in 2007, the up-to-date information about ecosystem and biodiversity, in particular,
flora biodiversity is urgently needed for conservation strategies as well as policy making
process in resource management. This study aims to evaluate Skidmore et al. (2015)’s proposed
variables to assess the ecosystem health in the Ca river basin from remote sensing indices,
namely leaf area index, soil moisture, net primary production, land use and fire occurrence with
the hypothesis that this framework is generic for all ecosystems. The remotely sensed imagery
was retrieved from Landsat 7 ETM+ SLC-off in March of three years, namely 2005, 2010 and
2015. In addition, a weighted scoring approach has been attempted to assess the vigor aspect of
ecosystem health. The results showed that the LAI was underestimated, which might imply that
the function retrieving LAI from SAVI for all crops was not applicable in this study area. In
addition, the calculation of soil moisture should be taken into account the weather condition

since they were estimated in a day only. Accordingly, the fire occurrence map also pointed out
some areas where the fire events happened at least twice in three time steps, which might be
caused by slash and burn practices of local inhabitants to prepare for the next crop. Related to
ecosystem health assessment in 2010 and 2015 in comparison with 2005, which is considered
a benchmark, the ecosystem of the study site in 2010 was moderate healthy and getting viable
in 2015. On the other hand, Skidmore et al (2015)’s approach remains with technical and
conceptual limitations. Likewise, it is believed that an agreement on an optimal resolution, in
terms of temporal, spatial and spectral resolutions, should be drawn among research
communities to bridge the gaps between remote sensing experts and ecological users.
Furthermore, the weighted scoring approach should be integrated between professional
biologists as well as the statistical data from the field to minimize the bias. In the end, local
calibration is a must since every ecosystem has its own characteristics and the algorithms might
not be applied to all ecosystems.

Keywords: Ecosystem Health Assessment, remote sensing, weighted scoring approach.

i


Acknowledgements
I would like to thank Dr. Hans van der Kwast for his support and many helpful contributions
over the past five months. His suggestions on how and where to find support for this study was
a major contributing factor to its completion. A special thanks to Tim Hessels who encouraged
me and engaged his time in technical issues. Together with intensive assignments and meetings
almost every week, their requirements have been keeping me on track with the thesis working
so that I could finish my thesis on time and engaged myself in scientific research related to
remote sensing.
This paper would not have been possible without Professor Wim G.M. Bastiaanssen,
who first inspired me to this thesis topic from a lecture given in early last year and guided me
with the theoretical concepts on remote sensing and biodiversity.

I would also like to acknowledge Adeline, my best friend and my classmate, for assisting
with many questions that I had on English writing and discussion. We did have a lot of
memories when working in DOK, TU Delft library since morning until midnight with nice
coffee and food.
A special thanks to Louis who is sharing with my all the sorrows we had when struggling
with thesis writing. We did have several stories to tell about life, about love, about tears, even
about the relationship between the duck-canal network in Delft with biodiversity. I will miss
the time we travelled together to Berlin and worked hard with our thesis on the bus.
A special thanks to Jam, Mariel, Clara, Shabana, Saltana and other Water Management
classmates who always take care of me and spent crazy time with me in last 18 months.
Last but not least, I would like to leave the last paragraph to give all my love to my
family, who always encourages and are beside me unconditionally. Finally, I would like to give
a big hug to my boyfriend, Quốc Trạng, who did encourage me to get this scholarship in last
two years and supports me with love and humor, smile and tears, strengths and efforts to
overcome all the obstacles in my life. I love you.

iii


Table of Contents
Abstract

i

Acknowledgements

iii

List of Figures

ix

List of Tables

xi

Abbreviations

xiii

Introduction

1

1.1. Background

1

1.2. Problem statement

5

1.3. Objectives

6

1.4. Hypothesis

6

1.5. Research questions

7

1.6. Study site background

7

1.7. Structure of the thesis

8

Literature Review
2.1. Ecosystem Health Assessment (EHA)

9
9

2.2. Skidmore et al. (2015)’s proposed biodiversity variables

10

2.3. Remote sensing indices as required inputs

12

2.3.1. Normalized Difference Vegetation Index (NDVI)

12

2.3.2. Soil Adjusted Vegetation Index (SAVI)

13

2.3.3. Normalized Burned Ratio (NBR)

14

2.3.4. Land surface temperature

15

2.4. Using indices to retrieve biodiversity variables

16

2.4.1. Leaf area index (LAI)

16

2.4.2. Leaf Nitrogen Content

16

2.4.3. Soil moisture

17
v

2.4.4. Land cover

18

2.4.5. Vegetation height

19

2.4.6. Burn severity levels

20

2.4.7. Vegetation phenology

21

2.4.8. Net primary production

22

2.4.9. Inundation

22

Case study site description

25

3.1. Location

25

3.2. Ecohydrological characteristics

26

3.2.1. Topography, geology and soils

26

3.2.2. Climate

26

3.2.3. Flora and fauna

27

3.2.4. Land use and land cover

29

3.2.5. Pressures, threats and current outlook

30

Methodology

33

4.1. Research strategy

33

4.2. Data collection, processing and analysis

35

4.2.1. Description of sensors available

35

4.2.2. Preprocessing Landsat 7 ETM+ SLC-off

36

4.3. Retrieving remote sensing indices and biodiversity variables from Landsat 7
ETM+

39

4.3.1. Estimating land surface temperature

40

4.3.2. Soil moisture

42

4.4. Burn severity levels and fire occurrence

43

4.5. Zonal statistics

43

4.6. Ecosystem Health Assessment

44

Results

47

5.1. Derivation of biodiversity variables from remote sensing

47

5.1.1. Leaf Area Index (LAI)

47

5.1.2. Soil Moisture

51
vi


5.1.3. Burn severity levels and fire occurrence

55

5.1.4. Net primary production

57

5.2. Ecosystem Health Assessment using Weighted Scoring approach
Discussion

60
65

6.1. Derivation of biodiversity variables from remotely sensed imagery.

65

6.2. Limitations of Skidmore et al. (2015)’s framework and suggested solutions for
improvement

68

6.2.1. Technical limitations

68

6.2.2. Conceptual limitations

70

Conclusions and Recommendations

75

7.1. Conclusions

75

7.2. Recommendations

76

References

77

Appendices

95
Conversion DNs to TOA brightness temperature

95

Python Scripts

97

Net primary production and World Net Primary produtivity for major
ecosystems

103

vii


List of Figures
Figure 1-1. Ecosystem services (Sheet 7) in Water Accounting Plus Framework .................... 3
Figure 1-2. The research flowchart ............................................................................................ 7
Figure 2-1. NDVI and SAVI calculated from a Landsat TM5 image of south-western Idaho 13
Figure 2-2. Spectral response curves of vegetation and burned area ....................................... 14
Figure 2-3. Flowchart of global vegetation classification logic ............................................... 19
Figure 2-4. Conceptual representation of a forest standing indicating the relative positions of
mean canopy height and scattering phase center height within a single SRTM resolution
cell ..................................................................................................................................... 20
Figure 3-1. Location and topographic map of the case study .................................................. 25
Figure 3-2. Location of Phu Xai Lai Leng ............................................................................... 26
Figure 3-3. Climatogram of study area, data at Vinh station (2013) ....................................... 27
Figure 3-4. World Biosphere Reserve Western Nghe An ........................................................ 27
Figure 3-5. Representative flora and fauna in study area. ........................................................ 28
Figure 3-6. The trend in land use structure from 2000 to 2013 ............................................... 29
Figure 3-7. Land Use map of Ca River Basin, Nghe An, Vietnam (2012) .............................. 30
Figure 4-1. The flowchart of retrieving biodiversity variables from remote sensing .............. 34
Figure 4-2. The difference of with and without SLC in processing image .............................. 36
Figure 4-3. Inverse Distance Weight Interpolation based on weighted sample point distance
(left) and Interpolated IDW surface from elevation vector points (right) ......................... 37
Figure 5-1. Leaf Area Index in three time steps (2005, 2010, 2015) ....................................... 48
Figure 5-2. Mean LAI in three time steps per land use ............................................................ 48
Figure 5-3. Soil moisture in three time steps ........................................................................... 51
Figure 5-4. Soil moisture per land use in three time steps ....................................................... 52
Figure 5-5. Burn severity levels of study area in three time steps ........................................... 55

Figure 5-6. Frequency of fire occurrence from 2005 to 2015 .................................................. 56
Figure 5-7. Net primary production in three years (2005, 2010, 2014) ................................... 57
Figure 5-8. Average net primary production per land use in three years. ................................ 58
Figure 6-1. Spatial and temporal resolution of both ecological processes and remote-sensing
observation ........................................................................................................................ 69
ix


List of Tables
Table 1.1. Examples of regulation services................................................................................ 2
Table 1.2. Ten proposed biodiversity variables in Skidmore et al. (2015)’s framework ........... 5
Table 2.1. Ten proposed biodiversity variables by Skidmore et al. (2015) ............................. 11
Table 2.2. Typical NDVI values for various cover types ......................................................... 13
Table 4.1. Radiometric range of bands and resolution for the ETM+ sensors ......................... 35
Table 4.2. Data preparation for this study ................................................................................ 37
Table 4.3. ESUN value for Landsat 7 sensor ........................................................................... 39
Table 4.4. List of formulas and methods used in the paper ..................................................... 40
Table 4.5. Ordinal severity levels and example range of dNBR (scaled by 103) ..................... 43
Table 4.6. Illustration of approach used in ecosystem health assessment ................................ 44
Table 4.7. Illustration of weighted scoring approach for the ecosystem of vigor .................... 45
Table 5.1. Leaf area index per land use in three time steps ..................................................... 50
Table 5.2. Coefficients of the polynomial relationship for Mo between T* and fc .................. 51
Table 5.3. Soil moisture per land use in three time steps ......................................................... 54
Table 5.4. Net primary production per land use in three years ................................................ 59
Table 5.5. Ecosystem health assessment of LAI in 2010 and 2015 ......................................... 61
Table 5.6. Ecosystem health assessment of soil moisture in 2010 and 2015 ........................... 62
Table 5.7. Ecosystem health assessment of NPP in 2010 and 2014 ........................................ 63
Table 5.8. Weighted scoring for the vigor of ecosystem health in 2010 and 2015 .................. 64
Table 6.1. A combination between indicators of ecosystem health and .................................. 71

xi


Abbreviations
APAR

Absorbed Photosynthetically Active Radiation

ASTER

Advanced Spaceborne Thermal Emission and Reflection Radiometre

ATM

Airborne Topographic Mapper

AVHRR

Advanced Very-High Resolution Radiometre

CGIAR

Consultative Group on International Agricultural Research

ETM+

Landsat Enhanced Thematic Mapper Plus

EOS

Earth Observing System

GPP

Gross Primary Productivity

INSAR

Interferometric Synthetic Aperture Radar

LAI

Leaf Area Index

LIDAR

Light Detection and Ranging

LST

Land Surface Temperature

MODIS

Moderate-resolution Imaging Spectro-radiometre

NASA

The U.S. National Aeronautics and Space Administration

NBR

Normalized Burned Ratio

NPP

Net Primary Production

NDVI

Normalized Difference Vegetation Index

PAR

Photosynthetically Active Radiation

SAR

Synthetic Aperture Radar

SAVI

Soil Adjusted Vegetation Index

SEBAL

Surface Energy Balance Algorithm for Land

SRTM

Shuttle Radar Topography Mission

SVIs

Spectral Vegetation Indices

SWIR

Short-wave Infrared

TIR

Thermal Infrared

TM

Landsat Thematic Mapper

V/NIR

Visible/Near Infrared

WLE

Water, Land and Ecosystem

xiii


CHAPTER 1

Introduction
1.1. Background
The Ca River Basin functions as ‘water tower’ with forest in the upstream and delta in the
downstream. The change of biodiversity and water resources in the forest will directly affect
the delta downstream in terms of natural disasters, water quality and salination in estuaries.
Urbanization and deforestation cause a loss in biodiversity and a reduction of vegetation cover,
as well as impacts on the hydrological cycle (e.g. interception, runoff, ET). Therefore, it is
believed that up-to-date information on changes can facilitate the policy makers in water
resources management and planning. In this sense, the data from earth observation will play an
important role in providing the actual and historical information.
Accordingly, ecosystem services are defined as the benefits people obtain from
ecosystems (Boyd & Banzhaf, 2007) or the combined actions of species in an ecosystem
performing functions of value to society (CGIAR, 2014), including provisioning, regulating,
cultural and supporting services. To emphasize the role of ecosystem services in natural
resources management and in economy, Costanza et al. (2014) highlighted that the valuation of
ecosystem services is not the same as commodification or privatization, they are best considered
public good requiring new institution. From other perception, Water, Land, and Ecosystems
(WLE) focus more on ecosystem as “common pool resources” which emphasizes the impacts
of collective action or large-scales intervention (CGIAR, 2014). WLE also taken into account
the horizontal flow of Ecosystem Services and Resilience framework (as the given example in
Table 1.1) to investigate the impact of biophysical structure and processes.

Introduction

1


Table 1.1. Examples of regulation services
Source: The Consultative Group on International Agricultural Research (2014)

Ecosystem
services
categories

Example of
ecosystem services
studied by WLE

Plot, farm
and smallcatchment
scale
approaches

WLE (Landscape scale
approaches)

Regulating services
Regulation of Natural drainage
water flows
irrigation and
drought prevention

Infiltration and
storage
capacity of
cropping
systems and
field
management
practices

Impacts of groundwater regulation,
wetland system (e.g. Tonle Sap),
riparian forest, protected forest
areas on flow regulation; impact of
landscape-level on water quality,
extent of riparian forest and field
margin management needed to
capture and store excessive nutrient
loads and ensure water quality.

Climate
regulation

Greenhouse
gas (GHG)
sequestration
of cropping
systems

GHG sequestration of alternative
land-use compositions and
configuration

Carbon
sequestration,
influence of
vegetation on
infiltration and
rainfall

Accordingly, Bastiaanssen et al. (2015)’s Water Accounting Plus (WA+) framework
has clarified the ecosystem services through the water consumption and non-consumptive use
of an ecosystem (Fig.1-1). In the latter section, the numbers of flora biodiversity are used as an
input to estimate water regulating services.

Introduction

2


Figure 1-1. Ecosystem services (Sheet 7) in Water Accounting Plus Framework
Source: www.wateraccounting.org
Introduction

3


Since the 1990s of last century, thanks to the development of space technology, remote
sensing and its applications has been acknowledged in several domains, such as agriculture (e.g.
land use, water accounting in irrigation), transportation (e.g. google map). In fact, with the
remote sensing analysis, the end-users, namely policy makers, management board, scientists,
are able to manage and control the planning at different administrative scales based on the data
derived from satellite images. The applications of remote sensing are first and foremost for
land-use classification, forest fire detection, urbanization. Kennedy et al. (2009) concluded the
vital role of remote sensing in natural resources management as the provision of consistent
measurements of landscape condition, which allows detection of both abrupt changes and slow
trends over time. Besides, Stork & Samways (1995) clarified two major areas where monitoring
biodiversity is applicable: the assessment of the effectiveness of biodiversity management
which aims to preserve and optimize biodiversity for other goals (e.g. plantation) by national

or regional program. The latter concentrates more on developing an early-warning system in
impending adverse changes before they become too critical.
Recently, papers on addressing the biodiversity, ecosystem status and effects of climate
variables are noticed: assessing effects of climate change (Bakkenes et al., 2002), forest change
detection (Ivits & Koch, 2002; Deslcée et al., 2006), ecosystem services (Peterson, 1997; Groot
et al., 2012), ecosystem health assessment (Rapport et al., 1998; Lu & Li, 2003; Ding et al.,
2005). Strand et al. (2007) emphasized the ability to detect change in vegetation cover and the
associated habitat with this cover by the alteration in the remote sensing signal from one-time
period to another. Busby (2003) also questioned the fundamental challenges in translating the
definitions about biodiversity into operational program along with what a high biodiversity
value is. However, these studies shared the shortcomings in the communication between remote
sensing experts and ecologists or biologists relating to an agreement on indicators, which can
lead to the insufficient results drawn by remote sensing users. Skidmore et al. (2015) call on an
agreement on a definitive set of “biodiversity variables between conservation and space
agencies as well as how these will be tracked from space, to address conservation target”.
Therefore, in this study, Skidmore et al. (2015)’s framework to detect biodiversity will be used.
This framework included ten proposed variables for satellite monitoring which are grouped into
four categories, named as species population, species traits, ecosystem structure and ecosystem
functions, as described in Table 1.2.

Introduction

4


Table 1.2. Ten proposed biodiversity variables in Skidmore et al. (2015)’s framework
Aspects
Species populations

Proposed variables

Species occurrence

Species traits (plant traits)

Specific leaf area
Leaf nitrogen content

Ecosystem structure

Ecosystem distribution
Fragmentation and heterogeneity
Land cover
Vegetation height

Ecosystem function

Fire occurrence
Vegetation phenology (variability)
Primary productivity and leaf area index
Inundation

In this research, a part of this framework was applied to evaluate ecosystem health in
the Ca river basin, consisting of four proposed variables: leaf area index, net primary
production, fire occurrence, land cover. In addition, soil moisture was added since it is also a
key component in flora biodiversity processes. Likewise, the definitions of “ecosystem health”
mentioned in this study have been aligned with the concepts of stress ecology, in which ‘health’
is related to system organization, resilience and vigor, as well as the absence of sign of
ecosystem distress (Rapport, 1989). In other words, a healthy ecosystem might have the ability
to maintain its structure (organization), function (vigor) and landscapes level over time in face
of external stress (resilience), as highlighted in Ding et al. (2005)’s research.

1.2. Problem statement
Traditionally, flora biodiversity was quantified and qualified by biologists and ecologists. In
this regard, it takes years for field investigations in the high biodiversity area, which might lead
to insufficient information for the planning and management processes. Besides, natural
parameters (e.g. precipitation, soil moisture, surface temperature) are often not taken into

Introduction

5


consideration. This study will evaluate the approach from Skidmore et al. (2015) with proposed
biodiversity variables to assess ecosystem health.

1.3. Objectives
The main objective of this study is to evaluate Skidmore et al. (2015)’s framework in ecosystem
health and flora biodiversity assessment in the Ca river basin (Vietnam), which can provide
policy makers and stakeholders up-to-date information for planning, management and
conservation strategies.
To clarify, three sub-objectives have been established:
1. To determine five variables that represent ecosystem structure, and ecosystem function

named as (i) leaf area index, (ii) soil moisture, (iii) fire occurrence, (iv) land cover, and (v)
net primary production.
2. To examine Skidmore et al. (2015)’s framework in the study area.
3. To assess ecosystem health and flora biodiversity using three indicators related to vigor,

organization and resilience of an ecosystem

1.4. Hypothesis
The main working hypothesis is that Skidmore et al. (2015)’s framework is applicable for the
Ca river basin (Vietnam) in evaluating ecosystem health and flora biodiversity status, using five
biodiversity variables, as described in Fig.1-2.

Introduction

6


REMOTE
SENSING INPUTS

BIODIVERSITY
VARIABLES

Q1
Calculation

Leaf Area Index

NDVI

SAVI

Land cover
Q2
Soil moisture

NBR

Surface temperature

ECOSYSTEM HEALTH
ASSESSMENT

Fire occurrence
Net primary production

Figure 1-2. The research flowchart

1.5. Research questions
Based on the addressed problems, the following research questions are proposed:
1. How can the three key environmental variables (LAI, soil moisture, fire occurrence) be
derived from remote sensing images?
2. How can ecosystem health be assessed using Skidmore et al. (2015)’s approach?
3. What are the limitations of Skidmore et al. (2015)’s approach and how can it be
improved to be more generic?

1.6. Study site background
The Ca river basin is the third largest river basin of Vietnam. This basin merges from the
highlands of Lao People Democratic Republic (PDR) and flows into the Tokin Sea. This basin
encompasses 27,200km2 in which 65% located in Vietnam, covering the province Nghe An,
Thanh Hoa, Ha Tinh, Quang Binh. The river basin endowed with tropical rainfall patterns,
annual average basin-wide rainfall of 1650 mm/year (Bastiaanssen et al., 2015). The majority
Introduction

7


of landscapes consists of natural vegetation, among others forests, bushland, grassland, and

herbaceous cover. The delta consists essentially of build-up areas, paddy fields and fish ponds.
Data collection process for flora biodiversity in Ca river basin is mainly based on historical data
and ground-based fieldwork, which is time-consuming and requires human resources (e.g.
biologists, ecologists). Therefore, a strategy to provide up-to-date information and a quick
change detection on flora biodiversity should be launched to solve this problem.

1.7. Structure of the thesis
In chapter one, the Introduction, the research background is presented in relation to ecosystem
health assessment, Skidmore et al. (2015)’s framework to evaluate biodiversity as well as the
research objectives, research questions and hypothesis of the research. Furthermore, in chapter
two, the Literature Review will be carried out to evaluate related studies on biodiversity and
remote sensing. The third chapter will describe the case study site with detailed ecohydrological
characteristics. Besides, based on reviews mentioned above, chapter Four will continue with
the research strategy in which approaches and method used to solve research questions are
explained. In the next two chapters, results will be depicted with interpretation and discussed
to address the main findings and limitations. At the end, conclusions will be drawn to address
the significance of the thesis regarding biodiversity evaluation and ecosystem health assessment
as well as recommendation for future research on the same domains.

In brief, an overall background of the thesis was introduced in this chapter with key words
related to biodiversity evaluation, ecosystem health assessment, remote sensing. Research
objectives, research questions and the main hypothesis are the backbone for the whole thesis
to be taken in the following chapters.

Introduction

8


CHAPTER 2

Literature Review
This chapter aims to provide a review of related studies in the same fields, including four main
sections: ecosystem health assessment, Skidmore et al. (2015)’s proposed biodiversity
variables, remote sensing indices and their applications to retrieve biodiversity parameters.

2.1. Ecosystem Health Assessment (EHA)
In general, an healthy ecosystem was defined in view of different disciplines and could be
divided into two types: biological – ecological definition and ecological – economic definition
(Peng et al., 2007). The former definition suggested by Rapport et al. (1998) emphasizes the
natural ecological aspects through the ranges of biological physics, ignoring the social
economics parts and human health. By contrast, the latter definition highlights the natural
ecosystem regarding to the human demands and requirements (Liu et al., 2008; Coutts & Hahn,
2015). As above-mentioned, ecosystem health can be assessed through the measurement of
vigor, organization, and resilience. Vigor emphasizes the measurement of activity, metabolism
or primary productivity; whereas organization may be estimated by the diversity and number
of interactions between system components. Besides, resilience or counteractive capacity can
be addressed as a system’s capacity to maintain the structure and function in the presence of
stress until reaching an adaptation tipping point, then the system will alter to an alternative state
(Rapport et al., 1988).
Additionally, not threatening to the other surrounding ecosystem and meeting the
reasonable needs of humankind (Jorgensen et al., 2005), human health effects (Rapport et al.,
2009) should be taken into account. Human health itself might be evaluated as a ‘synoptic’
measure of ecosystem health. In this sense, a healthy ecosystem is characterized by their
capacity to sustain healthy human populations. In brief, at present, it is acknowledged that the
concept of health portrays the vitality of sustainability development as well as the core of
integrated ecosystem management and ecosystem services (Peng et al., 2007).

Literature Review

9


To date the cooperation between remote sensing and ecology, the spatial-temporal scale
characteristics of ecosystems are taken into considerations since scale issue is one of the key
components of recent ecological researches (Lu & Fu, 2001). In this regard, ecological remote
sensed-base assessment would be focus on two main upper scales: (i) landscape/region in which
the effect of landscape spatial pattern to ecological processes and the dynamic maintenance of
ecosystem services functions is engaged; whereas (ii) global scale spotlights the relationship
between ecosystem services functions and human demands (Coder et al., 2003; Peng et al.,
2007). To put it more specifically, studies at global scale might facilitate the understanding of
global ecosystem health trend and public awareness, but the local features will be missed. As a
consequence, it would be difficult to assess and reflect ecosystem health. For this reason,
landscape/region scale might be preferable and become the key scale in ecosystem health
assessment by connecting macro-(globe) and micro-(ecosystem) scales.
Indicant (indicator) species method and indicator system method are widely applied to
evaluate ecosystem health (Kong et al., 2002; Peng et al., 2007). To clarify, the indicant species
method concentrates on the quantity, productivity and structural function (e.g. keystone species,
area-limited ‘umbrella’ species, resource-limited species or endangered species in a certain
ecosystem) (Peterken, 1974; Kremen, 1992; Gerald & McDonald, 2004). Consequently, this
approach failed to reflect the ecosystem health as a whole since socio-economic factors and
human health are not taken into consideration. On the contrary, based on the characteristics of
an ecosystem and its service function, an indicator system in which quantitative evaluation is
undertaken will be established. Selected indicators of the system mostly consist of ecosystem
structure, function, and process along with indicators about socio-economic, landscape pattern,
and land use (Peng et al., 2007).

2.2. Skidmore et al. (2015)’s proposed biodiversity variables
In order to assess the progress toward the Aichi Biodiversity Target for 2011 – 2020 set by the
Convention on Biological Diversity (Secretariat of the Conservation on Biological Diversity,

2010), Skidmore et al. (2015) call on an agreement on a definitive set of “biodiversity variables
between conservation and space agencies as well as how these will be tracked from space, to
address conservation target”. These variables at the first step are retrieved after two workshops

Literature Review

10


in Germany and Italy which aims to bring remote sensing experts and ecology communities to
generate the list.
Table 2.1. Ten proposed biodiversity variables by Skidmore et al. (2015)
Indicators

Skidmore et al. (2015)

Remote sensing
indicator

References

Species
population

Species occurrence

Plant traits

Leaf area index

NDVI

(Carlson & Ripley, 1997;
Bastiaanssen, 1998;
Turner et al., 1999;
Boegh et al., 2012)

Leaf nitrogen content

Red edge

(Cho & Skidmore, 2006;
Mutanga & Skidmore, 2007;
Clevers & Gitelson, 2013)

Soil moisture

Triangle or
trapezoid methods

(Moran et al., 1994; Carlson,
2007)

Land cover

Multispectral
reflectance

(Townshend et al., 1991;
Running et al., 1995)

Vegetation height

SAR images,
Sentinal1a

( Prevot et al., 1993;
Wegmüller & Werner, 1997;
Baghdadi et al., 2001;
Kellndorfer et al., 2004)

Fire occurrence/ Burn
severity level

Normalized Burned ( Jaiswal et al., 2002;
Ratio (NBR)
Roy et al., 2006;
Hernandez et al., 2006;
Key & Benson, 2006

Vegetation phenology

NDVI time series
(MODIS)

(Menenti et al., 1993;
Roerink et al. 2000; Zhang et
al., 2003)

Net primary produtivity

Biomass
production model

(Goetz et al., 1999;
Bastiaanssen & Ali, 2003;
Zhao et al., 2005)

Ecosystem
structure

Ecosystem
function

Inundation

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Ten variables proposed are divided into four main aspects: (i) species occurrence, (ii)
plant traits, (iii) ecosystem structure and (iv) ecosystem function. However, species occurrence
was taken out because it requires also information about flora and fauna biodiversity
relationship which take more time to measure. In addition, fragmentation and heterogeneity
criterion in ecosystem structure is formulated the expected outcome to evaluate flora
biodiversity. Moreover, a so-called criterion soil moisture is added to the list owing to its vital
roles in water management and quantifying surface temperature and water stress index. Table

2.1 addresses the remote sensing indicators which can be derived from remote sensing in order
to identify the biodiversity variables proposed by Skidmore et al. (2015).

2.3. Remote sensing indices as required inputs
2.3.1. Normalized Difference Vegetation Index (NDVI)

Normalized Difference Vegetation Index (NDVI), first used by Rouse et al. (1973), is mostly
used to determine the density of green on patch of land by earth observers. Researchers have to
differentiate the distinct colors (wavelengths) of visible and near infrared sunlight reflected
by the plants (Weier & Herring, 2000). Calculations of NDVI for a given pixel ranges
from minus one (-1) to plus one (+1); however no green leaves gives a value close to zero.
NDVI is calculated by:
𝑁𝐷𝑉𝐼 =

𝑁𝐼𝑅 − 𝑅𝑒𝑑
𝑁𝐼𝑅 + 𝑅𝑒𝑑

(1)

In this regard, chlorophyll absorbs light in the red channel (0.58 – 0.68 µm) and foliage reflects
light in the near infrared channel (0.72 – 1.10 µm). As a result, higher photosynthetic activity
will result in low reflectance in the red channel and higher reflectance in the near infrared
channel (Holben, 1986). Likewise, typical NDVI values for some cover types are presented in
Table 2.2

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Table 2.2. Typical NDVI values for various cover types
Source: Holben (1986)
Cover type

Red

NIR

NDVI

Dense vegetation

0.1

0.5

0.7

Dry bare soil

0.269

0.283

0.025

Clouds

0.227

0.228

0.002

Snow and ice

0.375

0.342

-0.046

Water

0.022

0.013

-0.257

2.3.2. Soil Adjusted Vegetation Index (SAVI)

In areas where vegetative cover is low (e.g. under 40%) and the soil surface is exposed, the
reflectance of sunlight in the red and near-infrared spectra can influence vegetation index value.
The SAVI is structured similar to NDVI but with the addition of a ‘soil brightness correction
factor’. L is a correction factor which ranges from 0 (for very high vegetation cover) to 1 (very
low vegetation cover). The most typically used value is 0.5 which is for intermediate vegetation
cover. This value minimizes the influence of background soil for a large variation of
leaf area indices (Huete, 1988).
𝑆𝐴𝑉𝐼 =

𝑁𝐼𝑅 − 𝑅𝑒𝑑
(1 + 𝐿)
𝑁𝐼𝑅 + 𝑅𝑒𝑑 + 𝐿

(2)

where NIR is the reflectance value of the near infrared band, RED is the reflectance of the
red band, L is the soil brightness correction factor. When L = 0, then SAVI = NDVI.

Figure 2-1. NDVI and SAVI calculated from a Landsat TM5 image of south-western Idaho
(Source: )
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This image shows a section of South-Fork Owyhee River canyon. NDVI image results in a high
index value in the rocky river canyon which addresses much more vegetative cover than is
actually there. On the other hand, the SAVI shows a much better approximation of the amount
and cover of vegetation in the canyon as well as in the upland.

2.3.3. Normalized Burned Ratio (NBR)

Normalized Burned Ratio was developed by Key & Benson (2006) by integrating band 4 (Near
Infrared) and band 7 (Shortwave Infrared) of Landsat TM/ETM+ sensor. This index is
calculated as:
𝑁𝐵𝑅 =

𝑁𝐼𝑅 − 𝑆𝑊𝐼𝑅

𝑁𝐼𝑅 + 𝑆𝑊𝐼𝑅

(3)

Band 4 reflectance naturally reacts positively to leaf area and plant productivity,
whereas band 7 reflectance positively responds to drying and some nonvegetated surface
characteristics (Fig. 2-2). In this sense, band 7 has low reflectance (it is absorbed) over green
vegetation and moist surfaces, including wet soil and snow – just the opposite from band 4.
NBR measures the difference between band 4 and 7. It is positive when band 4 is greater than
band 7, most vegetated areas are productive. When it is near zero, it can occur with clouds, non
productive vegetation (cured grasses), and drier soils or rock. The value is negative, which
suggests severe water stress in plants and the nonvegetative traits created within burns.

Figure 2-2. Spectral response curves of vegetation and burned area
Source: United States Forest Service (USFS, 2009)
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