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Impacts of Road Construction on Mangrove Structure in Atasta,
Mexico Using GIS and Landsat TM Imagery
A thesis submitted in partial fulfillment of the requirements
for the honours degree of Bachelor of Science
at Trent University
Peterborough, Ontario
Amber Brant
April 2011
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TABLE OF CONTENTS
Abstract………………………………………………………………………………………………………………………………………………….3
List of Tables…………………………………………………………………………………………………………………………………………..4
List of Figures……………………………………………………………………………….………………………………….………………….…5
Acknowledgements………………………………………………………………………………..………………………………….………….7
1.0 INTRODUCTION……………………………………………………………………………………………………………………8
1.1 Status, ecology and disturbance of mangroves……………………………………………………………….……8
1.2 Urban development in Campeche, Mexico…………………………………………………………………………14
1.3 Remote sensing in mangrove research ……………………………………….………………………….………....15
1.4 Research question…………………………….………………………….……………………………….……………………18
1.5 Research objectives……………….……………………………………….………………………………………………....19
1.6 Hypotheses and predictions……………………...…………………...………………………………………………...20
1.7 Approach………….……………………………………….……………………………………….……………………………….21
2.0 METHODS……………………………………………………………………………………………………………………….…..22
2.1 Study area………...………………………………………………………….………………………………………….…………22 2.2 Field sampling……….……………….............……………………………………………………………………………...27
2.3 Simpson’s biodiversity of mangroves………….…………………………………………………….……...………30 2.4 Remote sensing methods…………………...……………………………………………………………………….…...31 2.5 Distance-effect analysis……………….…….……………..……………………………………..………………....…..35
3.0 RESULTS…………………………………………….………………………………….…….......................................................37
3.1 Mangrove composition……………………………………………………………………………………………………….37
3.2 Selection of suitable SVI predictors…………………………………………………………………………………….40
3.3 Estimation and change in test variables……………………………………………………………………....…….42
3.3.1 Relative abundance of R. mangle………………………………………………………………………..42
3.3.2 Relative abundance of A. germinans…………………………………………………………………..47
3.3.3 Simpson’s biodiversity of mangroves………………………………………………………………….52
3.6 Distance-effect correlations………………………………………………………………………………………………..57
3.6.1 Change in R. mangle with distance from road……………………………………………..…..…57
3.6.2 Change in A. germinans with distance from road………………………………………………..60
3.6.3 Change in Simpson’s biodiversity with distance from road………………………..…….…63
4.0 DISCUSSION………………………………………………….………………………………………………………………………...66
4.1 Effect of road on R. mangle and A. germinans ………………………………………………………………..66
4.2 Effect of road on Simpson’s biodiversity………………………………………………………………..………..69
4.3 Efficacy of SVI in prediction of test variables …………………………………………………………………….70
4.4 Limitations of study……………………………………………………………………………….………………………….73
5.0 CONCLUSIONS AND RECOMMENDATIONS……………………………………..………………………………..….….74
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6.0 REFERENCES…………………..………………………………………………………………….………………………….………75
7.0 APPENDICES……………….………………………………………………………………………….….…………….…………….80
6.1 Appendix A: Raw field data…………….………………….……………………………………………..……..…80
6.2 Appendix B: ANOVA results in curve fitting for candidates for suitable SVI
for predicting relative abundance of R. mangle…….……………………………………………….……81
6.3 Appendix C: ANOVA results in curve fitting for candidates for suitable SVI
for predicting relative abundance of A. germinans.……………………………………………….……87
6.4 Appendix D: ANOVA results in curve fitting for candidates for suitable SVI
for predicting Simpson’s biodiversity (1-D) of mangroves………….........………………….……92
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Abstract
A road was constructed through a mangrove forest in Atasta Lagoon, Mexico in 1986. The
impacts of the road on mangrove structure were examined using field sampling, multispectral satellite
image analysis and GIS applications. It was hypothesized that the construction of the road negatively
impacts the: i) relative abundance of red mangrove (Rhizophora mangle); ii) relative abundance of black
mangrove (Avicennia germinans); and iii) biodiversity of all four species of mangrove.
Sixteen 900m2 field quadrats were sampled in the lagoon for total abundance of each of the four
mangrove species: R. mangle, L. racemosa, A. germinans and C. erectus L. Simpson’s biodiversity index
(1-D) was determined for each field plot. Regression analyses were used to select a suitable spectral
vegetation index (SVI) for predicting relative abundance of R. mangle, of A. germinans and Simpson’s
biodiversity of the four species. NDWI, GEMI and EVI were selected for predicting relative abundance of
R. mangle (R2=0.34, p<0.05, df=15), A. germinans (R2=0.37, p=0.13, df=15) and Simpson’s biodiversity
index (R2=0.64, p<0.01, df=15), respectively.
Using eight 650-m digital transects in ArcGIS, change from 1984-2009 for each of the three
variables were correlated with distance from road. There were significant correlations between distance
from road and change in R. mangle in 3 of the 8 transects (p<0.05) and A. germinans in 2 of the 8
transects (p<0.05). No significant correlations were found between distance and Simpson’s biodiversity.
Results demonstrate that relative species abundance and Simpson’s biodiversity of mangroves can be
effectively predicted using SVI for large-scale change studies. Results suggest that the road negatively
impacts the relative abundance of R. mangle. The impacts of the road on relative abundance of A.
germinans and biodiversity of mangroves are inconclusive.
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List of Tables
Table 1.1 Wavelengths and spatial resolution of the 7 bands detected by Landsat 5 TM ........................10 Table 2.1 Center coordinates of 30x30m quadrats for inventory of mangroves…….............................……20 Table 2.2 Selected spectral vegetation indices (SVI) used as candidates to predict the three
test variables……………………………………………………………………………………………………………………….…25 Table 3.1 Statistics for estimates and change of relative abundance of R. mangle in Atasta
Lagoon for 1984 and 2009.......................................................................................................…37 Table 3.2 Statistics for estimation and change in relative abundance of A. germinans in Atasta
Lagoon for 1984 and 2009.……..............................................................................................……42 Table 3.3 Statistics for estimated values and change in Simpson’s biodiversity (1-D) in Atasta
Lagoon for 1984 and 2009......................................................................................................…47
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List of Figures
Figure 2.1 Location of study area in southeast Mexico within the APFFLT………………………………………….…15 Figure 2.2 Lagoons found within the APFFLT ……………………………....................................................…………15 Figure 2.3 Display of Landsat 5 TM Band 5 (1.55-1.75 µm) for 24-year progression of change before
and after the construction of a road in the Atasta Lagoon..………………………………………………..…16 Figure 2.4 Regeneration on abandoned road crossing the mangrove forest.………………………….…………..…17 Figure 2.5 Locations of the 30x30m field plot for inventory of mangroves.……………………………………………19 Figure 2.6 Display of pixels with 30-m resolution for Landsat TM Band 5.……………………………………..………20 Figure 2.7 Locations of digital transects in ArcGIS.……………….……………………………………………………..…………27 Figure 3.1 Total mangrove abundance counts and corresponding Simpson’s biodiversity index
values (1-D) for 30x30m quadrats in Atasta Lagoon…………………….………………………….………..…29 Figure 3.2 Predictive relationship between NDWI and relative abundance of R. mangle.………………....…32 Figure 3.3 Predictive relationship between GEMI and relative abundance of A. germinans…………………32 Figure 3.4 Predictive relationship between EVI and Simpson’s biodiversity index (1-D)………………….……32 Figure 3.5 Estimated relative abundance of R. mangle in Atasta Lagoon for 1984 and 2009 using NDWI-based arithmetic operations.….……………………………………………………………………….……...…34 Figure 3.6 Estimated change in relative abundance of R. mangle in Atasta Lagoon using
NDWI-based arithmetic operations..……………………………………………….……………………………………35 Figure 3.7 Frequency distribution of estimated values for relative abundance of R. mangle in
Atasta Lagoon for 1984 and 2009.………………………………………………………………………………..………36 Figure 3.8 Frequency distribution of estimated values for change in relative abundance of
R. mangle in Atasta Lagoon from 1984-2009.……………….………………………………………………………36 Figure 3.9 Estimated relative abundance of A. geminans in Atasta Lagoon for 1984 and 2009 using GEMI-based arithmetic operations.….……………………………………………………………………….……...…39 Figure 3.10 Estimated change in relative abundance of A. geminans in Atasta Lagoon using
GEMI-based arithmetic operations..……………………………………………….……………………………………40 Figure 3.11 Frequency distribution of estimated values for relative abundance of A. geminans in
Atasta Lagoon for 1984 and 2009.………………………………………………………………………………..………41
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Figure 3.12 Frequency distribution of estimated values for change in relative abundance of A. geminans in Atasta Lagoon from 1984-2009.……………….………………………………………..…………41
Figure 3.13 Estimated Simpson’s biodiversity (1-D) of mangroves in Atasta Lagoon for 1984 and 2009
using EVI-based arithmetic operations.….…………………………………………………………….…….…...…44 Figure 3.14 Estimated change in Simpson’s biodiversity (1-D) of mangroves in Atasta Lagoon using
NDWI-based arithmetic operations..……………………………………………….……………………………………45 Figure 3.15 Frequency distribution of estimated values for Simpson’s biodiversity (1-D) of
mangroves in Atasta Lagoon for 1984 and 2009.…………………………………………………………………46 Figure 3.16 Frequency distribution of estimated values for change in Simpson’s biodiversity (1-D)
of mangroves in Atasta Lagoon from 1984-2009.……………….……………………………………….………46 Figure 3.17 Regression curves for correlation between distance from road and change in relative
abundance of R. mangle in Atasta Lagoon from 1984 to 2009……………………………………..………49 Figure 3.18 Regression curves for correlation between distance from road and change in relative
abundance of A. germinans in Atasta Lagoon from 1984 to 2009…………………………………..…..52 Figure 3.19 Regression curves for correlation between distance from road and change in
biodiversity of mangroves in Atasta Lagoon from 1984 to 2009…………………………………….……55
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Acknowledgements
First and foremost, I would like to thank my supervisor of the Department of Geography, Dr.
Raul Ponce-Hernandez. His encouragement in the imagination and organization of this project is now
invaluable to me. This being my first international research project, Dr. Ponce allowed me to overcome
any borders that presented itself in the logistics of solidifying a study like this. I would also like to give
Dr. Ponce-Hernandez my full gratitude for all of the experiences that has been made available to bring
this project to life, including the trip with Trent University’s Integrated Watershed Management course
in May 2010 to Campeche, Mexico, where many of the ideas presented here were first developed.
Finally, I would like to thank Dr. Ponce-Hernandez for the countless conversations in epistemology; I am
a better philosophy student because of these exchanges.
My gratitude also goes to Dr. David Beresford, my supervisor in the Department of Biology.
While Dr. Ponce provided much technical and educational support, Dr. Beresford was also invaluable in
the clarification of ideas that surfaced during the course of this project.
Dr. Angel Sol-Sanchez, of the Colegio Postgraduados Tabasco was also a key player in this
project. His field assistance and logistical support throughout my stay in Mexico will be always
appreciated. Also, Mario Dominiguez of the Colegio Postgraduados Tabasco was a huge support in the
field, and his help is very much appreciated throughout this process.
I would like to give Estrella Perez (M.Sc. candidate, Baja California) my fullest gratitude for her
generous help with the field aspect of this project. Her assistance was invaluable, and my Spanish has
improved because of her. Also, thanks must be given to Asuncion, the owner of the fishing boat in
which we used to travel throughout the lagoon. Edgar Tomes of the geospatial department for the
Colegio Postgraduados Tabasco was also a huge help in the GIS support for this project, as well as a huge
help in field support.
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1.0 INTRODUCTION
1.1 Status, ecology and disturbance in mangroves
Mangroves are coastal forests that are found in estuaries, along riverbanks and in shallow
lagoons in 124 countries worldwide (FAO, 2007). They are the only forest systems that can inhabit the
harsh buffer zone between terrestrial and ocean environments (Alongi, 2002). There is speculation
surrounding the evolution of mangroves, but a popular opinion is that their origins are terrestrial trees
in the Indo-west Pacific just after the arrival of the angiosperms 114 million years ago, where extended
periods of wetness allowed for a transition from dry to brackish adaptations in these plants (Kathiresan
and Bingham, 2001). Mangrove fossils are also found in areas where they no longer exist, like Texas, USA
and Western Australia, demonstrating that mangroves have persisted through many paleoclimatic
events and associated changes (Kathiresan and Bingham, 2001).
Mangroves have been studied extensively over the past 40 years, mostly under the subject of
productivity and community ecology, but the recent worldwide extent of mangrove forests has been
under review and has been inaccurately represented in the past, due to lack of inclusiveness and reliable
data (FAO, 2007). Giri et al. 2010 used high quality Landsat, Quickbird and Ikonos satellite images to
estimate the worldwide extent of mangroves in 2000 at 13.8 million ha using data from 118 countries,
the most comprehensive and reliable estimate made to date. There are approximately 741,917 ha of
mangrove forests in Mexico, which accounts for 5.4% of the global mangrove coverage (Giri et al. 2010).
The coast along the Gulf of Mexico is more humid than that of the Pacific coast, and the high diversity
and abundance of mangrove forests along the gulf reflects this difference in humidity (López-Portillo and
Ezcurra, 2002). Mangrove forests found in the states of Veracruz, Tabasco and Campeche typically have
a greater average height and species richness of mangroves, as the temperature in these states rarely
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falls below 14°C (López-Portillo and Ezcurra, 2002). In the state of Campeche, the extent of mangroves is
estimated at 196,552 ha as of 2009 (CONABIO, 2009), which is 26% of the country’s total extent, as
calculated from Giri et al. 2010.
Coastal ecosystems in the tropics are undergoing an increasing amount of change. Increasing
population pressure and demands for economic stability in tropical countries have placed these
ecosystems in a vulnerable state. Based on the limitations for mangrove persistence worldwide, the
future of this taxonomic group is influenced largely by climate change and by anthropogenic
manipulations (Alongi, 2002).
Mangroves have many ecosystem services that are economically and socially valuable. They
shelter floods from entering the inland areas along coasts and when the forests are large in stem
diameter, survival rates from tsunamis are high (Yanagisawa et al. 2009). With a large stem size,
inundation of water inland from a tsunami in Thailand was reduced by 30% when the tsunami depth was
less than 3m high (Yanagisawa et al. 2009).
There are six types of mangrove forest, based on water inputs and topography: riverine (river-
based), fringe (ocean-based), basin (interior), overwash (island), hammock and scrub, all of which
provide different ecosystem services (Lugo and Snedaker, 1974; Ewel et al. 1998). In terms of
maintaining ecosystem integrity, mangroves trap sediments, process nutrients from freshwater systems
and provide essential habitat for many wildlife species (Ewel et al. 1998). Riverine forests are most
important for conservation in terms of sediment trapping, because they are in the closest proximity to
freshwater systems and in their absence, sedimentation of particles can lead to erosion and offshore
deposition of these particles (Ewel et al. 1998). The above-ground biomass of basin mangrove forests is
higher than any other aquatic ecosystem, rivalling even the densest rainforests (Alongi, 2002). Fringe
forests protect shorelines and provide food and habitat for wildlife (Ewel et al. 1998).
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Mangrove-fishery linkages may be the most economically-biased justification for their
conservation. The state of Campeche, Mexico produces one sixth of Mexico’s entire total shrimp output
and the shrimp fishery sector employs 13% of the employed population in the state (Barbier, 2000). The
main nurseries for these shrimp are the mangrove forests located around the Términos Lagoon, Mexico
and with an estimated 2km2 loss of these forests per year, costs associated with decreases in shrimp
harvesting are estimated at $150,000USD per year (Barbier, 2000).
The value of mangroves as units of conservation has not been easy to assess in the past, due to
lack of complete knowledge about their function and how they adapt to change over time. In most
cases, conservation is location- and economy-specific. For example, in 1995 the CINVESTAV-IPN Unidad
Merida and the EPOMEX Program of the Universidad Autonoma de Campeche outlined four main
services provided by mangroves of the state of Campeche: use as timber resource for housing and
charcoal production (estimated at $451USD/ha/year for charcoal and $631USD/ha/year for housing),
fishery provisions (estimated at $1578 USD/ha/year), water filtering services (estimated at
$1193USD/ha/year) and habitat for critical wildlife (Cabrera et al. 1998).
Mangroves are a unique taxonomic group both structurally and functionally. Adaptations and
attributes include: aerial prop roots, salt/water/carbon regulation, tide-dispersed propagules, viviparous
embryonic reproduction and rapid canopy growth (Kethiresean and Bingham, 2001; Alongi, 2002). Many
factors affect how mangroves are distributed at different spatial scales. In the global scale, mangroves
are limited by temperature and humidity (can only occupy areas between 30° S and 30°N latitudes)
while at the regional scale, by rainfall and tidal frequency (Kathiresan and Bingham, 2001; Alongi, 2002).
At a local scale, because they require inflow of nutrients from freshwater sources and well-circulated
water flows, they are typically found in well-drained alluvial soils in well-sheltered areas (Kathiresan and
Bingham, 2001).
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Like most forest communities, mangroves are organized in distributional patterns related to
species type. In these forests, species richness is very low; the understory layer is filled with seedlings of
the overstory species, but the functional understory of herbaceous and shrub species does not exist
(Alongi, 2002; Krauss et al. 2008). They are found in different levels of abundance and growth rates,
depending on a suite of environmental conditions which include: frequency of floods, salinity, level of
and soil anoxia (Krauss et al. 2008).
Mean leaf area size in the red mangrove (Rhizophora mangle) in Mexico is positively correlated
with annual precipitation and latitude (Kathiresan and Bingham, 2001). Following the clearcut of a red
mangrove forest on the north coast of Para, Brazil, Berger et al. 2006 demonstrated that after years of
succession involving different non-mangrove species, white mangrove and then black mangrove were
able to enter the area and establish themselves successfully, but even after approximately 10 years, the
red mangrove cannot enter the area that it previously inhabited.
López-Portillo and Ezcurra (1989) conducted a study in the Mecoacán Lagoon, just west of
Frontera in the state of Tabasco, Mexico, investigating the effect of salinity on height and diameter of
the red (R. mangle), white (Laguncularia racemosa) and black mangrove (Avicennia germinans) found in
the area. Surrounding the lagoon, there are basins that are low in salinity and mudflats that are high in
salinity (López-Portillo and Ezcurra, 1989). A. germinans was found in higher relative abundance in the
mudflats, while all three species were found in evenly distributed abundances in the basin areas (López-
Portillo and Ezcurra, 1989).
R. mangle is restricted to intertidal zones and lagoons of humid tropical countries (Dominguez et
al. 1998). Variations observed in R. mangle due to habitat specialization include: tree morphology,
dominance in ecosystem structure, leaf area size and fruit size (Dominguez et al. 1998). Flowers are
observed all year round in this species and seedlings typically establish close to the parent tree
(Dominguez et al. 1998). Depending on resource availability, R. mangle can alter its leaf morphology,
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photosynthetic rates, plant stature and uptake of nutrients in an area (Farnsworth and Ellison, 1996).
Under high canopy enclosure, this species can slow its growth rate in shade, and take advantage of a gap
in the canopy, transitioning to high growth rates (Farnsworth and Ellison, 1993).
Globally, the main issues surrounding mangrove conservation include: clearing for urban
expansion and tourism, species introductions (R. mangle in Florida), road construction, agricultural
conversion, oil pollution, erosion, storm damage, conversion for aquaculture and use of herbicides
(Farnsworth and Ellison, 1997).
Disturbance is natural process that occurs in forest ecosystems, necessary for function and
promotion of species composition and succession that follows as a consequence (Sousa, 1984). The
discrimination between disturbance and stress is important to establish in terms of mangrove function
and change. Natural disturbances observed in mangrove ecosystems include: hurricanes, lighting strikes,
tidal fluctuations, extreme flood events (Sherman et al. 2000). Mangroves are considered very resilient
in the face of natural disturbances due to the following adaptations: storage of reservoir nutrients
below-ground, high biotic turnover due to nutrient fluxes, internal re-use of resources like water and
nutrients, rapid reconstruction post-disturbance, high abundance of keystone species associated with
mangrove ecosystems and finally, positive and negative feedbacks that allow flexibility (Alongi, 2008).
Despite their resilience, however, mangroves are as susceptible as any other forest ecosystem
to stress-inducing disturbance. Ellison and Farnsworth (1996) outline four classes of anthropogenic
disturbance that have been observed for mangrove ecosystems. Firstly, large-scale extraction for wood
products and fishery provisions alter soil pH and disrupt food-web linkages, respectively (Ellison and
Farnsworth, 1996). Second, large-scale pollution events from petroleum, metals and sewage results in
massive defoliation followed by tree death at all biological stages of life (Ellison and Farnsworth, 1996).
Third, reclamation in the form of land use change, including agriculture, urban development, tourism
and aquaculture, disrupts mangrove ecosystems through deforestation or alteration of the forests
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(Ellison and Farnsworth, 1996). Lastly, climate change impacts such as elevated CO2 levels, sea-level rise,
temperature increase and high-frequency storm events all contribute to changes in mangrove
ecosystems, effects that need further research to accurately address the issues (Ellison and Farnsworth,
1996).
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1.2 Urban development in Campeche, Mexico
Until 1976, the main activities observed in the region of the Términos Lagoon were forestry,
agriculture and small-scale fishery practices (Bach et al. 2005). Mexico has been involved with oil
extraction, refinement and exportation for approximately 40 years, following the 1971 discovery of
Cantarell, the major hydrocarbon deposit in the marine platform of the Términos Lagoon (Soto-Galera et
al. 2010). Oil production began in the Sound of Campeche about five years after the discovery of
Cantarell and now produces 80% of crude oil and 30% of natural gas for all of Mexico (Soto-Galera et al.
2010). The rise of the oil industry in the state of Campeche has also influenced land use changes due to
increased urbanization and Petróleos Mexicanos (PEMEX) oil industry infrastructure (Soto-Galera et al.
2010). From 1974-2001, the two main causes for land change surrounding the Términos Lagoon were i)
increased urbanization and consequently agricultural land and ii) oil infrastructure establishment in
place of wetlands including mangroves (Soto-Galera, 2010). Mangrove forests decreased in extent in the
Términos Lagoon region by 13% from 1974-2001 (Soto-Galera et al. 2010). Oil and gas supplies in this
region will last approximately two more decades, at which point the state of Campeche will enter a new
economic state (Bach et al. 2005).
Urban development in the Téminos Lagoon area has had social and environmental impacts
including: extreme poverty and marginalization in the village of Atasta as an indirect result of PEMEX
influence, oil pipeline leaks, water pollution from agricultural runoff and sewage, land modification for
agriculture and cattle raising, construction of bridges between the island of Carmen and the mainland
that disrupt wetland functioning, illegal fishing, mangrove deforestation for timber and road
construction that restrict water flow between ecosystems (Bach et al. 2005).
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1.3 Remote sensing in mangrove research
There is a growing popularity in the use of remotely sensed data from satellites in large-scale
ecology studies (Aplin, 2005). The three main divisions of remote sensing in ecology are land
classification, ecosystem models using field measurements and land change detection (Aplin, 2005). The
basis of orbital remote sensing is the collection of information from platforms which then collect
electromagnetic energy from the Earth’s surface, which is then transmitted, recorded and separated
into bands with different wavelengths that can be then analyzed using geospatial software (Table 1.1)
(Campbell, 2007). The software displays pixels with corresponding digital numbers that represent
spectral signatures gathered from the ground (Campbell, 2007). For vegetative classification and long-
term vegetative change, especially at the species level, the sensors, Landsat Thematic Mapper (TM)
and Landsat Enhanced Thematic Mapper (ETM+) have advantages over the other sensors in studying
long-term changes in landscapes (Xie et al. 2008). Spatial resolution for a given sensor describes the
minimum distance between two objects on the ground that can be discriminated (Campbell, 2007). All
multispectral bands for Landsat TM data have 30-m resolution, and the thermal infrared band with 120-
m resolution. For species-level discrimination, the two best-suited sensors are the IKONOS and
Quickbird, with spatial resolutions of 4m and 2.4-2.8m for multispectral bands, respectively, but scenes
produced from these products are expensive and do not provide extensive historical data records (Xie
et al. 2008).
Vegetative mapping at the species level in heterogenous environments is challenging with
Landsat satellite imagery, but can be done with adequate field data calibration (Xie et al. 2008).
Remotely-sensed Landsat TM data have been used extensively in mangrove research. Mangrove
mapping research deals with the accurate determination of the extent of mangroves in an area,
facilitated by remote sensing methods (Long and Skewes, 1996). Landsat TM data have been used to
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classify mangroves (Long and Skewes, 1996; Ramírez-García et al. 1998; Sulong et al. 2002; Mas, 2004;
D’iorio et al. 2007; Lee and Yeh, 2009) and determine change in extent over time (Kovacs et al. 2001;
Béland et al. 2006). In these cases, the 30-m resolution of Landsat TM is suitable for the large-scale
applications that are involved.
The leaf area index (LAI) of mangrove trees can be measured in the field, and has been
effectively correlated with band ratios of wavelength bands provided by Landsat TM data (Díaz and
Blackburn, 2003). Spectral vegetative indices (SVI) are band ratios that have been developed, tested and
used for their effectiveness in detecting reflected radiant energy from the ground and used for
predicting physical properties on the ground (Myeni et al. 1995; Baugh and Groeneveld, 2006).
Produced from multiple bands, SVI are arithmetic expressions that can be computed from the
wavelengths bands produced by satellite sensors (Campbell, 2007). Band 4 (near-infrared) from the
Landsat TM sensor (expressed as TM4) has been used to represent vegetative density, greenness and
photosynthetic activity, as plants reflect energy at this wavelength (Mironga, 2004). TM3 can be used to
express leaf area, as it is related to the plant’s absorption of the Sun’s energy (Mironga, 2004). TM5
(shortwave-infrared) and TM7 (mid-infrared) represent wavelengths that are related to moisture
content on the ground, and can be used to estimate biomass of plants and canopy closure, as it detects
reflected energy from the soil as well as the soil as well as the canopy (Mironga, 2004). The Simple Ratio
index (TM4/TM3) and the Normalized Difference Vegetation Index (NDVI) (TM4-TM3/TM4+TM3) are
two examples of indices that have been tested for their reliability to estimate vegetative parameters
(Baugh and Groeneveld, 2006).
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Table 1.1 Wavelengths and spatial resolution of the 7 bands detected by Landsat 5 TM (Campbell, 2007).
Landsat 5 (TM sensor)
Wavelength (µm)
Resolution (meters)
Band 1 (Blue)
0.45 - 0.52
30
Band 2 (Green) 0.52 - 0.60 30 Band 3 (Red) 0.63 - 0.69 30
Band 4 (Near Infared) 0.76 - 0.90 30 Band 5 (Shortwave Infrared 1.55 - 1.75 30
Band 6 (Thermal) 10.40 - 12.50 120 Band 7 (Mid Infrared) 2.08 - 2.35 30
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1.4 Research question
An unpaved road was constructed in 1986 in the Atasta Peninsula, located south of the village of
Atasta, Campeche, Mexico. The road was constructed as a means for transportation of materials from
the north to the south end of the area, but is no longer in operation. The elevated soil platform is
approximately 5m above the remainder of the forest, to prevent inundation onto the road. Chemicals
like asphalt and concrete were not used in construction or maintenance and traffic was infrequent
during operation. The study site provides an exceptional opportunity to study a known disturbance in an
otherwise protected natural area. This study is the result of the research question, ‘how does the
constructed road impact surrounding mangrove structure in the area?’
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1.5 Research objectives
The overall goal of this study is to effectively examine the direct impacts of a constructed road
on surrounding mangrove structure in Atasta Lagoon, Campeche, Mexico using field sampling,
multispectral satellite image analysis and GIS applications. Change analysis as well as distance-effect
analysis will be the main methods to fully examine the effect of the constructed road. The specific aims
of the study are to: i) effectively predict canopy species composition and biodiversity of mangroves
using spectral vegetation indices (SVI) and ii) evaluate the effect of the road on change in species
composition of two dominant species as well as their biodiversity in the area.
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1.6 Hypotheses and predictions
The basis for this research is to gain understanding in how a constructed road impacts mangrove
structure, in terms of species composition and biodiversity. Three hypotheses are proposed to support
the research question and its underlying analysis. It is hereby proposed that the constructed road
negatively impacts the i) relative abundance of red mangrove (Rhizophora mangle); ii) relative
abundance of black mangrove (Avicennia germinans); and iii) biodiversity of all species of mangrove in
the forest along a gradient from the location of the road. All hypotheses are mutually exclusive and only
supported if canopy characteristics are effectively predicted using spectral vegetation indices (SVI).
- 21 -
1.7 Approach
Due to the size of the area and theme of the overall research project, remote sensing methods
were used to address the research question. Ground measurements of mangrove community structure
were integrated with multispectral satellite image analysis and GIS applications. This integration relies
on the statistical relationship between these ground measurements and spatial patterns detected by
multispectral satellite imagery, as well as manipulations of arithmetic formulas using multispectral
bands given in the image data.
- 22 -
2.0 METHODS
2.1 Study area
The study area lies in the village of Atasta in the state of Campeche, Mexico between 18°34’07
and 18°37’21 latitudes and -92°04’15 and -91°58’02 longitudes in the Área de Protección de Flora y
Fauna Laguna de Términos (APFFLT) (Figure 2.1). The Términos Lagoon, Atasta Lagoon, De Carlos
Lagoon, Pom Lagoon and Puerto Rico Lagoon are located within the APFFLT (Figure 2.2). Atasta Lagoon
has an area of 30 km2 with an average depth of 1.5 m, depending on the season (Ruiz-Marín et al. 2009).
The sediment type of the lagoon is muddy-clay and experiences the dry season from February to May,
rainy season from June to September and influence from north-east winds from October to January
(Ruiz-Marín et al. 2009). Temperatures range from 25-31°C in the area, depending on the season (Ruiz-
Marín et al. 2009).
The APFFLT hosts the largest density of mangrove species in the state (Vega, 2005). The canopy
vegetation is nearly 100% mangrove, with four species found within the study area: red (Rhizophora
mangle), white (Laguncularia racemosa), black (Avicennia germinans) and the less common button
mangrove (Conocarpus erectus L.). The red mangroves in the area are very common and typically
dominate the area, with black mangrove as a secondary species. The maximum heights reached by the
R. mangle and A. germinans are approximately 20m and 30m, respectively. The average approximate
age of these mangroves in the area is 20 years, with a few nearly 200 years old (personal
communication). Laguncularia racemosa and Conocarpus erectus L. are less common and reach
approximate maximum heights of 18m and 10m, respectively. Palm, banana and cacti are also common
in certain areas. Biomass and basal area of mangrove forests increases with increasing distance
westward from the Términos lagoon, as humidity increases (Barreiro-Güemes, 1999). The Atasta lagoon
is the second lagoon to the east, with little water renewal and dominated by large but sparse black
- 23 -
mangroves (Barreiro-Güemes, 1999). The De Carlos lagoon is a shallow area with red mangroves as
pioneers and black mangroves as the dominant species (Barreiro-Güemes, 1999).
The road is located a southwest direction from Federal Highway 180 (Figure 2.3). The road was
constructed as a means for transportation of materials from the north to the south end of the area, but
is no longer in operation. The elevated soil platform is approximately 5m above the remainder of the
forest, to prevent inundation onto the road. Chemicals like asphalt and concrete were not used in
construction or maintenance and traffic was infrequent during operation. Regeneration has taken place
on the abandoned road including banana (Musa sp.), Citrus sp., and noni (Morinda citrifolia), a plant
whose fruits have medicinal qualities (Figure 2.4).
The region received legal protection on June 6, 1994 through administrative efforts by the
“Comisión Nacional de Áreas Naturales Protegidas” (CONANP) (Vega, 2005). A small village with a
population of 2,096 as of 2005, Atasta is geographically located in close proximity to increasing urban
development and oil extraction activities and infrastructure (Implan, 2010). The current issues in Atasta
include: oil exploration and infrastructure leading to human affection and disease, decreasing fish
productivity, loss of critical habitats due to deforestation, low agricultural productivity and sulphur
dioxide emissions by PEMEX recompression stations in the area (Yáñez-Arancibia et al. 1999; Ruiz-Marín
et al. 2009). A gasline rupture in 1985 in the area of the recompression station caused an increase in
salinity in the area and consequently dry deposition of sulphur dioxide (Yáñez-Arancibia et al. 1999).
Social issues also are apparent in the area, with conflicts occurring between PEMEX and the Movement
of Fishers and Farmers of the Atasta Peninsula who have blockaded federal highway 180 demanding
compensation through public works (Bach et al. 2005).
- 24 -
Figure 2.1 Location of study area in southeast Mexico within the APFFLT.
Figure 2.2 Lagoons found within the APFFLT.
- 25 -
Figure 2.3 Display of Landsat 5 TM Band 5 (1.55-1.75 µm) for 24-year progression of change before and after the
construction of a road in the Atasta Lagoon.
1986-01-15 1999-01-19
1986-07-26 2009-11-30
1987-04-24 2010-02-02
- 26 -
Figure 2.4 Regeneration at south end of the abandoned road crossing the mangrove forest.
- 27 -
2.2 Field sampling
In order to examine the relationship between the road and its surroundings, an inventory of the
mangrove forest was made during the dry season, January 4-January 9th, using a boat to access the
channels between the mangrove forests. For the area, 17 field sampling points were selected prior to
data collection to calibrate with the information given in satellite imagery (Table 2.1, Figure 2.5). The
points were selected with a judgemental bias – clusters of three were identified throughout the area to
sample different habitats in order to maintain a high degree of heterogeneity in sampling.
At each field point, a 30 x 30 m quadrat was assembled, with center coordinates pre-determined
and used for navigation, corresponding to one pixel displayed in satellite imagery (Figure 2.6). A
handheld Garmin GPS receiver (GPSMAP 76CSx) was used to locate the field points with an average
position error of ±3.96 m. A compass was used to align the edges of the quadrats in north-south aspects.
Mangrove species were identified based on bark appearance, root structure and leaf
appearance. Identification was assisted by an experienced naturalist familiar with mangrove ecosystems
in the area. Abundance counts of adult R. mangle, L. racemosa, A. germinans, and C. erectus L. were
determined and recorded within each quadrat using four 15 x 15m sections within each plot. Only
mangroves with a height of at least 2m were included in the inventory. Due to the fact that mangroves
occupy nearly 100% of the overstory and understory canopy, other vegetation was noted but not
assessed. Field quadrat 5 was inaccessible by foot due to a high density of mangrove prop roots and
consequently omitted from further analysis.
- 28 -
Figure 2.5 Locations of the 30x30m field plot for inventory of mangroves.
- 29 -
Table 2.1 Center coordinates of 30x30m quadrats for inventory of mangroves.
Quadrat
Easting
(m)
Northing
(m)
Longitude
(degrees, minutes, seconds)
Latitude
(degrees, minutes, seconds)
1
599280
2059050
-92°03’31.9337
18°37’12.2438
2 599310 2058870 -92°03’30.9422 18°37’06.3828
3 599430 2058630 -92°03’26.8905 18°36’58.5545
4 601530 2057550 -92°02’15.4317 18°36’23.0569
5 601740 2057580 -92°02’08.2610 18°36’23.9962
6 601410 2056950 -92°02’19.6357 18°36’03.5583
7 599880 2056170 -92°03’11.9781 18°35’38.4473
8 599790 2056050 -92°03’15.0702 18°35’34.5588
9 599910 2055840 -92°03’11.0139 18°35’27.7064
10 601050 2054340 -92°02’32.3930 18°34’38.7110
11 601170 2054850 -92°02’28.2065 18°34’55.2818
12 601620 2054490 -92°02’12.9205 18°34’43.4919
13 602040 2055630 -92°01’58.3832 18°35’20.5055
14 602400 2055570 -92°01’46.1123 18°35’18.4905
15 602578 2055510 -91°01’40.0506 18°35’16.5072
16 603270 2058480 -92°01’15.8900 18°36’53.0059
17 603480 2058570 -92°01’08.7077 18°36’55.8965
Figure 2.6 Display of pixels with 30-m resolution for Landsat TM Band 5.
- 30 -
2.3 Simpson’s biodiversity of mangroves
Simpson’s biodiversity index (1-D) (SBI) was used to estimate biodiversity for each field plot. All
four species of mangrove were included in analysis of biodiversity. The index is geared towards
abundance of the dominant species, and therefore considered an indicator of dominance concentration
(Hill, 1973). This index is valuable in this case where the two dominant mangrove species are found in
high densities, with the other two species as secondary species. The equation for Simpson’s index is:
SBI =1 -
where ni = number of individuals in species i, n = total number of individuals
Values approaching 1 suggest high biodiversity and values approaching 0 suggest low
biodiversity (Hill, 1973).
- 31 -
2.4 Remote sensing methods
2.4.1 Satellite data
A Landsat TM satellite image acquired on November 30, 2009 was downloaded from the USGS
Global Visualization Viewer (http://glovis.usgs.gov/) for the location with path/row, 21/47 (with center
of swath at 18.8° latitude and -91.4° longitude) and used as the most recent cloud-free image that
corresponds with season for field collection. A second image with the same path and row was acquired
for the date November 25, 1984. The 1984 image was used along with the 2009 image for change
analysis, with the road construction occurring in 1986.
2.4.2 Pre-processing
The .tiff files were extracted and opened in PCI Geomatica© software for processing. The study
area was clipped from all bands within the image bundle with the following extents: 18°34’06 to
18°37’21 latitudes and -92°04’15 to -91°58’02 longitudes. Bands 1-5 and 7 were used for the study, all
having equal spatial resolution. A high-pass edge sharpening filter was passed on all bands with a 33x33
kernel size. This filtering process ensures that all pixel value possibilities (1-255) were used in digital
number representation, increasing variability in spectral signatures.
- 32 -
2.4.3 Processing of SVI
The selection of 16 SVI candidates originated from primary literature from which reliable
estimates of forest canopy characteristics have been demonstrated using calibration with field
measurements (Table 2.2). For each SVI, a map was produced in PCI Geomatica© using the band ratios
within the 2009-11-30 satellite image. Determination of values for each SVI was made using the raster
calculator within PCI’s interface for appropriate bands.
2.4.4 SVI Predictions for test variables
The 16 spectral vegetation indices (SVI) were tested for their strength in predicting each of the
three test variables: relative abundance of R. mangle, relative abundance of A. germinans and Simpson’s
biodiversity index (1-D). The SVI values produced using the 2009-11-30 image were considered in the
analysis. The 16 field-collected values for each of these were used as dependent variables in regression
curve fitting for linear, logarithmic, inverse, quadratic, cubic, compound, power, S, growth, exponential
and logistic relationships using SPSS Statistics 17.0 software. The suitable SVI for each test variable was
selected based on goodness-of-fit with the field data. Those with a high coefficient of determination (R2)
and low p-value based on ANOVA tests were used a qualifying candidates.
The resulting equations produced from regression analyses were computed in the PCI
Geomatica© raster calculator with the selected SVI as the independent variable to produce a map
displaying each of the three test variables. The same equation used for the 2009-11-30 satellite image
bundle was used also for the 1984-11-25 image bundle to produce maps displaying estimates of the
three test variables for this date. Finally, three change maps were produced displaying change in relative
- 33 -
abundance of R. mangle, change in relative abundance of A. germinans and change in Simpson’s
biodiversity (1-D) from 1984-2009. In total, nine maps were produced: six displaying estimates of each
of the three test variables in 1984 and 2009 and three displaying change in the three test variables from
1984 to 2009.
The nine raster maps were saved as .pix files and opened in ArcGIS for further analysis. The .pix
files were exported into raster format and clipped to the 2,660ha extent: 18°34’07 to 18°37’21 latitudes
and -92°02’50 to -92°00’18 longitudes for display purposes in proximity to the road.
- 34 -
Table 2.2 Selected spectral vegetation indices (SVI) used as candidates to predict the three test variables.
SVI
Formula for computation using digital numbers (DN) of
pixels in both images
TM4
Landsat 5 TM Band 4
TM5 Landsat 5 TM Band 5
TM7 Landsat 5 TM Band 7
Normalized Difference Vegetation Index (NDVI) (Gould, 2000)
([TM4-TM3] / [TM4+TM3])
Structure Insensitive Pigment Index (SIPI) (Sims and Gamon, 2002)
([TM4-TM1] / [TM4-TM3])
Simple Ratio (SR) (Myeni et al. 1995)
(TM4 / TM3)
Normalized Difference Water Index (NDWI) (Gao, 1996)
([TM4-TM5] / [TM4+TM5])
Chlorophyll Vegetation Index (CVI) (Vincini et al. 2008)
([TM4/TM2] * [TM3/TM2])
Green Vegetation Index (GVI) (Todd and Hoffer, 1998)
([TM4+TM5] / [TM3+TM7])
Mid Infrared Index (MIRI) (Feeley et al. 2005)
(TM5 / TM7)
Difference Vegetation Indices (DVI) (TM4-TM3) (Diaz and Blackburn, 2003) (TM4-TM2) (TM3-TM2)
Global Environmental Monitoring Index (GEMI)
(Pinty and Verstraete, 1992)
n(1 – (0.25*n)) – [(TM3 - 0.125) / (1 - TM3)] where n= [ 2*(TM42 – TM32) + (1.5*TM4)+(0.5*TM3)] /
(TM3 + TM4 + 0.5)
Enhanced Vegetation Index (EVI) (Huete et al. 1997)
2.5* [ (TM4 – TM3) / [TM4 + (6*TM3) – (7.5*TM1) + 1] ]
Atmospherically Resistant Vegetation Index
(ARVI) (Kaufman and Tanré, 1996)
[TM4 – (2*TM3 – TM1)] / [TM4 + (2*TM3 – TM1)]
- 35 -
2.6 Distance-effect analyses
In order to examine the effect of the constructed road on mangrove structure, 8 digital transects
were produced within ArcGIS as polyline shapefiles (Figure 2.7). The transects were positioned at 1000-
m intervals perpendicular to the road, with a length of 650 m each. Values were obtained for pixels
found every 30 m along the transect for the following variables: change in relative abundance of R.
mangle from 1984-2009, change in relative abundance of A. germinans from 1984-2009 and change in
Simpson’s biodiversity (1-D) from 1984-2009. In total, 22 values were found for each transect for each of
the three test variables.
The values found at each point along the transects were correlated with distance from road,
with each test variable as the dependent variable and distance as the independent variable. Linear
regression analyses were made for each transect with the three variables, with two divisions. Division 1
includes initial distance from road to a visible saturation point, thereafter the effect of the road appears
to stabilize in division 2. Significant correlations were those considered with a high coefficient of
determination (R2) and low corresponding p-value.
- 36 -
Figure 2.7 Locations of digital transects in ArcGIS.
- 37 -
3.0 RESULTS
3.1 Mangrove composition
Within the 16 field plots sampled, the four species of mangrove were found in various relative
abundances, the highest mangrove density located near the south of the lagoon (Figure 3.1a). The most
dominant mangrove species encountered was R. mangle with 966 individuals, followed by A. germinans
with 806 individuals. Secondary mangrove understory species included L. racemosa with 522 individuals
and C. erectus L. with 39 individuals.
There is a significant positive linear relationship between abundance of A. germinans and
abundance of R. mangle over the sixteen 900m2 field quadrats sampled (R2=0.274, p<0.05, df=15),
suggesting a lack of competition between these species in these areas (Figure 3.1b). There is no
significant relationship between abundance of R. mangle and Simpson’s biodiversity (R2=0.1267,
p=0.176, df=15) or between abundance of A. germinans and Simpson’s biodiversity (R2=0.0687, p=0.327,
df-15) (Figure 3.1c-d).
- 38 -
Figure 3.1a Total mangrove abundance counts (C. erectus L. , L. racemosa , A. germinans , R. mangle ) and corresponding Simpson’s biodiversity index values (1-D ) for 30x30m quadrats in Atasta Lagoon.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
25
50
75
100
125
150
175
200
225
250
275
300
1 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17
Quadrat #
Ab
un
dan
ceSim
pso
n's b
iod
iversity (1-D
)
- 39 -
Figure 3.1b Relationship between total abundance of A. germinans and R. mangle per 900m
2 field quadrats.
Figure 3.1c Relationship between total abundance of R. mangle and
Simpson’s biodiversity index (1-D) per 900m2 field quadrats.
Figure 3.1d Relationship between total abundance of A. germinans and
Simpson’s biodiversity index (1-D) per 900m2 field quadrats.
y = 0.8x + 19R² = 0.274
p<0.05, df=15
0
50
100
150
200
0 20 40 60 80 100
R. m
an
gle
A. germinans
y = 0.002x + 0.5R² = 0.1267
p=0.176, df=15
0
0.2
0.4
0.6
0.8
1
0 20 40 60 80 100 120 140
Sim
pso
n's
(1
-D)
R. mangle
y = 0.002x + 0.5R² = 0.0687
p=0.327, df=15
0
0.2
0.4
0.6
0.8
1
0 20 40 60 80 100
Sim
pso
n's
(1
-D)
A. germinans
- 40 -
3.2 Selection of Suitable SVI Predictors
For the prediction of relative abundance of R. mangle, NDWI was selected with a strong
logarithmic relationship (R2=0.3402, p=0.018, df=15), significant with 95% confidence and root-mean-
square (RMS) value of 0.0096 (Figure 3.2).
In predicting the relative abundance of A. germinans, GEMI was selected with a cubic
relationship (R2=0.3682, p=0.13, df=15), which has no statistical significance, but the goodness-of-fit and
RMS value of 0.0101 permit this SVI as the most suitable candidate (Figure 3.3). In the case of A.
germinans, MIRI demonstrates a higher predictive strength with a cubic relationship (R2=0.3811, p=0.11,
df=15), but includes several outliers that influence the curve in a visibly biased shift.
For the prediction of Simpson’s biodiversity index (1-D), the suitable SVI selected is EVI for its
strong quadratic relationship (R2=0.6394, p=0.0013, df=15), significant with 99% confidence and an RMS
of 0.0078 (Figure 3.4).
- 41 -
y = 0.85145234 ln(x) + 1.12787657
R2 = 0.3402p=0.018
0.0
0.2
0.4
0.6
0.8
1.0
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65
Rel
ativ
e ab
un
dan
ce o
f R
. ma
ng
le
NDWI
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Pre
dic
ted
Observed
RMS=0.0101y = 0.0000000000294x3 + 0.0000003027889x2
+ 0.0009694613309x + 1.3246147011149
R2 = 0.3682p=0.13
0.0
0.2
0.4
0.6
0.8
1.0
-8000 -6000 -4000 -2000 0
Rel
ativ
e ab
un
dan
ce o
f A
. ger
min
an
s
GEMI
0.0
0.2
0.4
0.6
0.8
1.0
0 0.2 0.4 0.6 0.8 1
Pre
dic
ted
Observed
RMS=0.00965
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Pre
dic
ted
Observed
RMS=0.0078y = -6.01044356x2 + 26.21671375x - 27.88633915
R2 = 0.6394p=0.0013
0.0
0.2
0.4
0.6
0.8
1.0
1.5 1.75 2 2.25 2.5 2.75 3
1-D
EVI
Figure 3.2 Predictive relationship between NDWI and relative abundance of R. mangle.
Figure 3.3 Predictive relationship between GEMI and relative abundance of A. germinans.
Figure 3.4 Predictive relationship between EVI and Simpson’s biodiversity index (1-D).
- 42 -
3.3 Estimation and change in test variables 3.3.1 Relative abundance of R. mangle
The estimated relative abundance of R. mangle is given for the years 1984 and 2009 in Figure
3.5. It was expected that there would be a theme of negative change in relative abundance of R. mangle
for the 29,500 pixels computed. There are three general clusters of land where drastic change occurs,
just west of the middle section of the road, southeast of the road and along the periphery of the road
itself (Figure 3.6).
The estimates for relative abundance of R. mangle in November 1984 and November 2009
follow symmetrical unimodal frequency distributions across the raster map (Figure 3.7). In November
1984, the estimated mean relative abundance of R. mangle for the 2,660 km2 area was 0.276 ± 0.201
per 900m2 section of land for the area, the equivalent of one pixel computed (Table 3.1). The estimated
mean relative abundance decreased to 0.255 ± 0.199 per 900m2 section of land in November 2009
(Table 3.1).
The overall estimated change in relative abundance of R. mangle from November 1984 to
November 2009 follows a symmetrical unimodal frequency distribution (Figure 3.8). The estimated
mean change in relative abundance from 1984 to 2009 is -0.021 ± 0.186 per 900m2 section of land,
which is a 7.6% decrease from the estimated 1984 value (Table 3.1).
- 43 -
Figure 3.5 Estimated relative abundance of R. mangle in Atasta Lagoon for 1984 and 2009 using NDWI-based arithmetic operations.
- 44 -
Figure 3.6 Estimated change in relative abundance of R. mangle in Atasta Lagoon using NDWI-based arithmetic operations.
- 45 -
Figure 3.7 Frequency distribution of estimated values for relative abundance of R. mangle in Atasta Lagoon for 1984 and 2009. N=29500 pixels.
Figure 3.8 Frequency distribution of estimated values for change in relative abundance of R. mangle in Atasta Lagoon from 1984-2009. N=29500 pixels.
- 46 -
Table 3.1 Statistics for estimates and change of relative abundance of R. mangle in Atasta Lagoon for 1984 and 2009. N=29500 pixels.
Year Minimum Maximum Mode Median Mean Standard Deviation
1984 0 1.000 0 0.324 0.276 0.201 2009 0 0.997 0 0.295 0.255 0.199
1984-2009 -1.000 1.000 0 0 -0.021 0.186
- 47 -
3.3.2 Relative abundance of A. germinans
It was expected that there would be positive change in relative abundance of A. germinans for
the 29,500 pixels computed. The estimated relative abundance of R. mangle is given for the years 1984
and 2009 in Figure 3.9. There are two general regions where observable change occurs: just west of the
middle section of the road and southeast of the road, in the same areas where there is decrease in
relative abundance of R. mangle (Figure 3.6; Figure 3.10). There is no visible change along the periphery
of the road for this species (Figure 3.10).
For both November 1984 and November 2009, the relative abundance estimates of A.
germinans follow right-skewed frequency distributions across the raster map (Figure 3.11). In November
1984, the estimated mean relative abundance of A. germinans for the 2,660ha area is 0.301 ± 0.204 per
900m2 section of land for the area (Table 3.2). The estimated mean relative abundance increased to
0.315 ± 0.222 per 900m2 section of land in November 2009 (Table 3.2).
The overall estimated change in relative abundance of A. germinans from November 1984 to
November 2009 follows a symmetrical trimodal distribution (Figure 3.12). The estimated mean change
in relative abundance from 1984 to 2009 is 0.014 ± 0.243 per 900m2 section of land, which is a 4.6%
decrease from estimated 1984 value (Table 3.2).
- 48 -
Figure 3.9 Estimation of relative abundance of A. germinans in Atasta Lagoon for 1984 and 2009 using GEMI-based arithmetic operations.
- 49 -
Figure 3.10 Estimated change in relative abundance of A. germinans from 1984 to 2009 in Atasta Lagoon using GEMI-based arithmetic operations.
- 50 -
Figure 3.11 Frequency distribution of estimated values for relative abundance of A. germinans in Atasta Lagoon for 1984 and 2009. N=29500 pixels.
Figure 3.12 Frequency distribution of estimated values for change in relative abundance of A. germinans in Atasta Lagoon from 1984-2009. N=29500 pixels.
- 51 -
Table 3.2 Statistics for estimation and change in relative abundance of A. germinans in Atasta Lagoon for 1984 and 2009. N=29500 pixels.
Year Minimum Maximum Mode Median Mean Standard Deviation
1984 0 0.977 0 0.348 0.301 0.204 2009 0 0.997 0 0.351 0.315 0.222
1984-2009 -0.977 0.977 0 0 0.014 0.243
- 52 -
3.3.3 Simpson’s biodiversity of mangroves
The estimated relative abundance of R. mangle is given for the years 1984 and 2009 in Figure
3.13. It was expected that there would be an overall negative change in Simpson’s biodiversity for the
29,500 pixels computed. There are three areas of land where visible change occurs: just west of the
middle section of the road and southeast of the road, in the same areas where there is change in
relative abundance of the two dominant species, and finally along the north periphery of the road
(Figure 3.14). For both November 1984 and November 2009, the Simpson’s biodiversity estimates follow
left-skewed frequency distributions across the raster map (Figure 3.15). In November 1984, the
estimated mean Simpson’s biodiversity for the 2,660ha area is 0.471 ± 0.282 per 900m2 section of land
(Table 3.3). The estimated Simpson’s biodiversity decreased to 0.441 ± 0.287 per 900m2 section of land
in November 2009 (Table 3.3).
The overall estimated change in Simpson’s biodiversity from November 1984 to November 2009
follows an asymmetrical trimodal distribution (Figure 3.16). The estimated mean change from 1984 to
2009 is -0.030 ± 0.242 per 900m2 section of land, which is a 6.4% decrease from the estimated 1984
value (Table 3.3).
- 53 -
Figure 3.13 Estimated Simpson’s biodiversity (1-D) of mangroves in Atasta Lagoon from 1984 to 2009 using EVI-based arithmetic operations.
- 54 -
Figure 3.14 Estimated change in estimation of Simpson’s biodiversity (1-D) in Atasta Lagoon using EVI-based arithmetic operations.
- 55 -
Figure 3.15 Histogram for distribution of estimated values for Simpson’s biodiversity (1-D) in Atasta Lagoon for 1984 and 2009. N=29500 pixels.
Figure 3.16 Histogram for distribution of estimated values for change in Simpson’s biodiversity (1-D) of mangroves in Atasta Lagoon from 1984-2009. N=29500 pixels.
- 56 -
Table 3.3 Statistics for estimated values and change in Simpson’s biodiversity (1-D) in Atasta Lagoon for 1984 and 2009. N=29500 pixels.
Year Minimum Maximum Mode Median Mean Standard Deviation
1984 0 0.702 0 0.627 0.471 0.282 2009 0 0.702 0 0.597 0.441 0.287
1984-2009 -0.702 0.702 0 0 -0.030 0.242
- 57 -
3.6 Distance-effect correlations
The overall effect of the road on each of the test variables were tested using ANOVA for linear
correlation. The overall effect was divided between a distance of 0-225m and 255-645m, based on
observations that saturation occurs at 255m, a distance after which the effect is predicted to be
stabilized. For points where it is believed that water is encountered, in areas where mangroves are not
found, the corresponding data points were omitted from analysis and the next point used in the trend.
Transects 7 and 8 are such examples, where distances of 105 and 135 m on transect 7 display water and
distances of 615 and 645 m on transect 8 display water. For all three test variables, the overall effect of
the road is considered to have an impact if at least two of the eight total transects for a given distance
division demonstrate a statistically and visibly significant correlation.
3.6.1 Change in R. mangle with distance from road
There are statistically and visibly significant positive linear correlations between distance from
the road and change in relative abundance of R. mangle between 1984 and 2009 observed in the 0-
225m division for transect 1 (R2=0.5691, p<0.05, df=7), 255-645m division for transect 4 (R2=0.3121,
p<0.05, df=13) and 0-255m division for transect 5 (R2=0.5462, p<0.05, df=7) (Figure 3.17a-b). For
transects 1-5, there are generally positive relationships between distance and change in abundance, up
to the threshold of 225m (Figure 3.17a-b). After the 225m point, the effect appears to be stabilizing
(Figure 3.17a-b). There is no evidence of any effect occurring in transect 6 for either division (Figure
3.17b). Finally, for transects 7 and 8, there appears to be a great deal of noise in the data, likely due to
habitat variability and water conditions that can be observed in Figure 2.7 (Figure 3.17b).
- 58 -
y = 8E-05x - 0.0095R² = 0.0025
p=0.907
y = -0.0005x + 0.3093R² = 0.1236
p=0.218
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700
Ch
ange
in R
. ma
ng
le
Transect 3
Distance (m)
y = 0.0014x - 0.3042R² = 0.456p=0.066
y = 0.0006x - 0.3019R² = 0.3121
p<0.05, df=13
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700
Ch
ange
in R
. ma
ng
le
Transect 4
Distance (m)
y = 0.0013x - 0.1446R² = 0.3841
p=0.101
y = -0.0003x + 0.0582R² = 0.0548
p=0.421
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700
Ch
ange
in R
. ma
ng
le
Transect 2
Distance (m)
y = 0.0022x - 0.4105R² = 0.5691
p<0.05, df=7
y = 0.0004x - 0.1097R² = 0.2344
p=0.079
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700
Ch
ange
in R
. ma
ng
le
Transect 1
Distance (m)
Figure 3.17a Regression curves for transects 1-4 for correlation between distance from road and change in relative
abundance of R. mangle in Atasta Lagoon from 1984 to 2009. Shaded are significant correlations.
- 59 -
y = -5E-05x + 0.0402R² = 0.0009
p=0.943
y = 0.0007x - 0.5458R² = 0.137p=0.236
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700
Ch
ange
in R
. ma
ng
le
Transect 7
Distance (m)
y = -0.0005x + 0.1631R² = 0.1897
p=0.281
y = -0.0001x + 0.1387R² = 0.0382
p=0.503
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700
Ch
ange
in R
. ma
ng
le
Transect 6
Distance (m)
y = -0.0008x + 0.0579R² = 0.3098
p=0.251
y = 0.0006x - 0.5926R² = 0.2701
p=0.057
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700
Ch
ange
in R
. ma
ng
le
Transect 8
Distance (m)
y = 0.0021x - 0.2424R² = 0.5462
p<0.05, df=7
y = -0.0002x + 0.1657R² = 0.0682
p=0.197
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700C
han
ge in
R. m
an
gle
Transect 5
Distance (m)
Figure 3.17b Regression curves for transects 5-8 for correlation between distance from road and change in relative abundance of R. mangle in Atasta Lagoon from 1984 to 2009. Shaded are significant correlations.
- 60 -
3.6.2 Change in A. germinans with distance from road
There are statistically and visibly significant positive linear correlations between distance from
road and change in relative abundance of A. germinans observed in division 255-645m for transect 1
(R2=0.3262, p<0.05, df=13) and in division 255-645m for transect 2 (R2=0.3648, p<0.05, df=13)
(Figure 3.18a). There are no statistically or visibly significant relationships observed in the remaining 6
transects (Figure 3.18a-b). In transects 1 and 8, there is a slight negative correlation between distance
from road and change in relative abundance for the 0-225m division, but only up to the 225m threshold
(Figure 3.18a-b). As observed with change in relative abundance of R. mangle, transect 6 demonstrates
no effect of the road, and transects 7 and 8 include a great deal of noise in the data (Figure 3.18b).
- 61 -
y = 1E-05x - 0.0057R² = 0.0017
p=0.924
y = 0.0002x - 0.0747R² = 0.0403
p=0.492
-0.3
-0.2
-0.1
0
0.1
0.2
0 100 200 300 400 500 600 700
Ch
ange
in A
. ger
min
an
s
Transect 3
Distance (m)
y = 4E-05x + 0.0285R² = 0.004p=0.882
y = 0.0007x - 0.2864R² = 0.3648
p<0.05, df=13
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0 100 200 300 400 500 600 700
Ch
ange
in. A
. ger
min
an
s
Transect 2
Distance (m)
y = 0.0005x - 0.027R² = 0.0158
p=0.767
y = -0.0003x + 0.1529R² = 0.1294
p=0.206
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0.6
0 100 200 300 400 500 600 700
Ch
ange
in A
. ger
min
an
s
Transect 4
Distance (m)
y = -0.0005x + 0.0585R² = 0.2835
p=0.174
y = 0.0001x - 0.0547R² = 0.3262
p<0.05, df=13
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0 100 200 300 400 500 600 700
Ch
ange
in. A
. ger
min
an
s
Transect 1
Distance (m)
Figure 3.18a Regression curves for transects 1-4 for correlation between distance from road and change in relative abundance of A. germinans in Atasta Lagoon from 1984 to 2009. Shaded are significant correlations.
- 62 -
y = -0.0009x + 0.1285R² = 0.4905
y = -0.0002x + 0.1164R² = 0.0071
p=0.775
-0.4
-0.2
0
0.2
0.4
0.6
0 100 200 300 400 500 600 700
Ch
ange
in A
. ger
min
an
s
Transect 8
Distance (m)
y = 0.001x - 0.1817R² = 0.2304
p=0.229
y = 5E-07x + 0.0175R² = 3E-06p=0.995
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0 100 200 300 400 500 600 700
Ch
ange
in A
. ger
min
an
s
Transect 5
Distance (m)
y = 6E-05x - 0.007R² = 0.0131
p=0.788
y = -5E-05x + 0.0165R² = 0.0372
p=0.509
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0 100 200 300 400 500 600 700
Ch
ange
in A
. ger
min
an
s
Transect 6
Distance (m)
y = 0.0011x - 0.0721R² = 0.4633
p=0.063
y = -0.001x + 0.6292R² = 0.3183
p=0.056
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0.6
0 100 200 300 400 500 600 700
Ch
ange
in A
. ger
min
an
s
Transect 7
Distance (m)
Figure 3.18b Regression curves for transects 5-8 for correlation between distance from road and change in relative
abundance of A. germinans in Atasta Lagoon from 1984 to 2009.
- 63 -
3.6.3 Change in Simpson’s biodiversity with distance from road There are no statistically or visibly significant correlations between distance from road and
change in Simpson’s biodiversity (1-D) in both divisions (Figure 3.19a-b). There is a general trend for
negative linear correlation for transects 1, 3, and 4 and positive linear correlation in transects 2 and 5 for
the first division (0-225m) (Figure 3.19a-b). After the 225m division, there is no visible trend between
distance and change in biodiversity in all transects (Figure 3.19a-b). Transects 6, 7 and 8 demonstrate no
trends for effect of the road on change in biodiversity, and transects 7 and 8 also include noise in the
data, like for the other test variables (Figure 3.19b).
- 64 -
y = -0.0007x - 0.1398R² = 0.0314
p=0.675
y = 0.0003x - 0.2017R² = 0.013p=0.698
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700
Ch
ange
in 1
-D
Transect 4
Distance (m)
y = 0.0003x - 0.12R² = 0.0143
p=0.778
y = -0.0002x + 0.0041R² = 0.0081
p=0.760
-0.6
-0.4
-0.2
-1E-15
0.2
0 100 200 300 400 500 600 700
Ch
ange
in 1
-D
Transect 2
Distance (m)
y = -0.0005x + 0.0441R² = 0.0801
p=0.497
y = -0.0004x + 0.169R² = 0.0647
p=0.380
-0.6
-0.4
-0.2
-1E-15
0.2
0.4
0 100 200 300 400 500 600 700
Ch
ange
in 1
-D
Transect 3
Distance (m)
y = -0.0004x + 0.0468R² = 0.3062
p=0.155
y = 3E-05x - 0.0158R² = 0.0077
p=0.766
-0.2
-0.1
0
0.1
0.2
0.3
0 100 200 300 400 500 600 700
Ch
ange
in 1
-D
Transect 1
Distance (m)
Figure 3.19a Regression curves for transects 1-4 for correlation between distance from road and change in biodiversity of mangroves in Atasta Lagoon from 1984 to 2009.
- 65 -
y = -0.0014x + 0.1629R² = 0.1126
p=0.417
y = 0.0004x - 0.1695R² = 0.1799
p=0.131
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0 100 200 300 400 500 600 700
Ch
ange
in 1
-D
Transect 6
Distance (m)
y = -0.0014x + 0.3151R² = 0.079p=0.500
y = 0.0012x - 0.763R² = 0.1155
p=0.280
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0 100 200 300 400 500 600 700
Ch
ange
in 1
-D
Transect 7
Distance (m)
y = 0.0007x - 0.1049R² = 0.1892
p=0.281
y = 0.0001x - 0.0793R² = 0.1100
p=0.247
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0 100 200 300 400 500 600 700
Ch
ange
in 1
-D
Transect 5
Distance (m)
y = 0.0023x - 0.366R² = 0.3107
p=0.250
y = 0.0004x - 0.1437R² = 0.0803
p=0.326
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
-1E-15
0.1
0.2
0.3
0 100 200 300 400 500 600 700
Ch
ange
in 1
-D
Transect 8
Distance (m)
Figure 3.19b Regression curves for transects 5-8 for correlation between distance from road and change in biodiversity of mangroves in Atasta Lagoon from 1984 to 2009.
- 66 -
4.0 DISCUSSION
4.1 Effect of road on R. mangle and A. germinans
Based on the ANOVA analyses for distance-effect correlations across the 8 digital transects, the
results suggest that the road negatively impacts the relative abundance of R. mangle for the 0-225m
sections in 2 transects, and the 255-645m section for 1 transect, in partial support of the first hypothesis
for this study. The results do not support the second hypothesis that the road negatively impacts the
relative abundance of A. germinans. There are two positive correlations for the 255-645m sections of
two transects, but the lack of evidence for a correlation with proximity to the road suggests either that
there is no effect or that there is a substantial amount of noise in the data transmission from field
collection and regression with the SVI to comparison with distance using ArcGIS.
There is no evidence of competition between the two dominant species, R. mangle and A.
germinans for the areas sampled in the field. Only riverine sections of the forest were sampled, along
the border of the lagoon, so this reflects only the dynamics occurring for these areas, where both
species can be found in mutually high abundances (Figure 2.5). The lack of relationship between
abundance of either of the dominant species and Simpson’s biodiversity confirms that the index is an
indication of evenness of species, not relative dominance of one (Hill, 1973).
Theoretically, the construction of the road would have three direct consequences on the
surrounding mangroves: modification of soil conditions, increase in light availability from gap
introductions and obstruction of water flow. Constructed pathways through mangrove forests have had
unforeseen and long-term indirect impacts in the past. A boardwalk constructed through mangroves
(Avicennia marina) in Australia modified macrofauna assemblages up to 24m from the boardwalk
(Kelaher et al. 1998a). With increasing distance from the boardwalk, there was also an increase in
- 67 -
pneumatophores density for this species, and crab species abundance was high near the boardwalk
(Kelaher et al. 1998b). These indirect impacts of a linear anthropogenic disturbance are consequences of
sediment changes during construction as well as increased light exposure to the soil (Kehalher et al.
1998a, 1998b).
The road would have created substantial gaps along the periphery of the elevated soil and water
table, making the mangroves found along the edge exposed to “edge effects”. In Rhizophora sp. and
Avicennia sp., exposure to an increased amount of sunlight leads to structural consequences in growth:
gnarled and lateral branching as well as increased root density (Duke, 2001). Under natural light
conditions, after the maturing phase in the mangrove, a thinning typically occurs, followed by
senescence after a given amount of years (Duke, 2001). Given the increased exposure to light that the
roads induced, the mangroves likely allocated their energy to lateral branching and increased root
density, accelerating the senescence phase (Duke, 2001). R. mangle was once thought as completely
shade-tolerant, but numerous studies have demonstrated that under varying light conditions, this
species is actually able to persist and re-establish itself following canopy gap generation and has been
suggested as being dependent on these gaps for persistence (Chen and Twilley, 1998). Koch (1997)
found that growth rates in R. mangle were 2-5x greater in canopy gaps created by hurricanes than in
closed canopies in the Everglades, Florida.
Community competition in mangrove forests has been modeled based on neighbourhood
dynamics. The ‘field of neighbourhood’ approach proposed by Berger and Hildenbrandt (2000) treats
mangroves as separate entities with overlapping ‘zones of influence’, which describe the tree spacing
effects that can be modelled to predict spatial patterns for R. mangle and A. germinans. The models
described conclude that if both species establish in an area at the same time, R. mangle dominates the
stand due to a faster growth rate, but is outcompeted by A. germinans decades later due to the longer
life span in this species (Berger and Hildenbrandt, 2000). If one species establishes in an area, followed
- 68 -
by the other species, the pioneer will always maintain dominance in the area (Berger and Hildenbrandt,
2000). It is unknown which species was first established in the Atasta Peninsula. If both species entered
and established in the lagoon, based on the logic proposed by this model, A. germinans has had a very
long period of time to become the dominant species in the area, and from which, R. mangle may be
suppressed along the highway due to advantages taken by A. germinans with the increased availability
of light.
Increased light is just one consequence of gap formation caused by mangrove deforestation. In
this case, elevation of the soil and water table would modify hydrology dynamics and soil conditions
surrounding the constructed road. Constriction of water flow may cause soil salinity to increase along
the periphery of the road, as the lagoon is brackish (NaCl concentration is lower than ocean water, but
higher than freshwater). A. germinans, with a high growth rate and high tolerance to high salinity
conditions, may have a competitive advantage over other species in this way (Lovelock and Feller, 2003).
- 69 -
4.2 Effect of road on Simpson’s biodiversity
Based on the ANOVA analyses for distance-effect correlations across the 8 digital transects, the
results suggest that the road has no effect on Simpson’s biodiversity (1-D) of the four mangrove species,
supporting the third null hypothesis. Given that the relationship between field-collected and SVI-
predicted values for this test variable was very strong, it is ample to state that the results suggest that
the road has no impact on overall evenness of mangrove species in the area. Noise from data
transmission in this case is likely very minimal, when compared to that of the prediction of relative
abundance of A. germinans.
The overall decrease in this variable by 0.03 units (4.6% from 1984 value) for the 2,660ha study
area should be considered with the units expressed using the Simpson’s biodiversity index (1-D) in mind.
The range for 1-D is 0.0 for no biodiversity to 1.0 for complete evenness of species, so a 4.6% decrease
from estimated values for 1984 is substantial.
Mangroves are steady-state ecosystems; they have the ability to persist through natural
stressors via positive and negative feedbacks. The mangrove ecosystem as a whole experiences stress on
a cycle; when stress is low in the absence of disturbance for a long period of time, species diversity is
highest, but is regulated by future periods of stress caused by disturbance (Lugo, 1980). Human-created
stressors, caused by disturbances such as the constructed road here, by comparison, create general
patterns of perturbation that cause an overall decrease in height and biomass of the mangroves,
resulting in a potential decrease in biodiversity over time (Lugo, 1980). The study period used here is 25
years. It is possible (and most likely) that the disturbance caused by the road poses a threat to overall
biodiversity if intensity and frequency is high, and may only be detectable after a long period of time,
after the senescence stage.
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4.3 Efficacy of SVI in prediction of test variables
The use of remote sensing for mangrove studies has come a long way in recent years, from
aerial photography to Landsat MSS and TM to high resolution sensors like SPOT, ASTER or IRS C and D
(Heumann, 2011). All methods still have their applicability for mangrove studies, depending on the
scope and scale of the research.
Rhizophora sp. reflect red and infrared energy approximately 10% more than Avicennia sp.,
making it possible to discriminate generalized patterns of relative density in mangrove forests (Blasco et
al. 1998). Due to atmospheric effects observed in satellite data, especially for areas where humidity is
high. All of the SVI selected include one or more factors that correct for atmospheric effects. The
selections for suitable SVI to predict the test variables were a result of a series of regressions for
goodness-of-fit and not extensive testing for their efficacy in predicting the field variables. In light of
this, the statistically significant correlations found for predicting relative abundance of R. mangle and
Simpson’s biodiversity (1-D) were both strong enough to apply to the whole area for hypothesis testing
in this study.
The Normalized Difference Water Index (NDWI), also named the Infrared Index (IRI), was used in
this study to predict relative abundance of R. mangle. The relationship between NDWI and abundance
of R. mangle in this study is not the tightest fit as anticipated, but in terms of predicting overall patterns
of R. mangle abundance for the area, it serves its purpose. The index is given as the subtraction of
wavelength 1.24µm by 0.86µm and divided by the addition of these two wavelengths (Gao, 1996). These
wavelengths correspond to the near infrared (Band 4) and mid-infrared (Band 5) wavelengths given in
Landsat TM data ([TM4-TM5] / [TM4+TM5]) (Jackson et al. 2004).Mid-infrared radiation is not absorbed
by plants and has a lower leaf transmission, so scattering is less apparent than with near-infarred
radiation (Steininger, 2000). The index has been effectively used to predict and monitor changes in corn
and soybean vegetative water content (VWC), even after NDVI, one of the most widely used indices
- 71 -
([TM4-TM3] / [TM4+TM3]), saturated and failed to detect changes (Jackson et al. 2004). NDWI is also
less sensitive to atmospheric effects than NDVI (Gao, 1996). The index has also been associated with dry
plant above-ground biomass in tropical forests of Brazil (R2>0.64, p<0.01) (Steininger, 2000) and relative
tree abundance in a semi-deciduous tropical dry forest in Venezuela (rs=−0.55, p<0.01) (Feeley et al.
2005).
The prediction of A. germinans by GEMI is not as strong as for the other two test variables, with
the lack of statistical significance. GEMI is an index with a complex nonlinear formula, unlike the other
SVI evaluated, with several factors using bands 3 and 4 as independent variables. Bands 3 and 4,
displaying red and infrared reflectance from the top of the atmosphere, are useful to use in combination
with one another, due to the low reflectance of energy in visible wavelengths of light and high
reflectance in the infrared (Pinty and Verstraete, 1992). The basic formula for comparing these bands is
TM4/TM3 (the Simple Ratio), which has been modified over time with testing to produce ratios with
higher complexity like NDVI and GEMI (Table 2.2) (Pinty and Verstraete, 1992). GEMI was proposed to
overcome the effects of atmospheric and illumination effects as well as bias to one vegetation type
(Pinty and Verstraete, 1992). In the case of the study presented here, the cubic trend observed between
reflected GEMI values and measured abundance of A. germinans is visibly apparent, but not statistically
strong enough to draw conclusions based on their correlation.
The quadratic relationship for prediction of Simpson’s biodiversity (1-D) of mangroves by EVI in
this study is very strong, relative to the two other variables evaluated. The Enhanced Vegetation Index
(EVI), also named the Soil And Atmosphere Resistant Vegetation Index (SARVI2), utilizes bands 1, 3 and 4
in its formula, for visible blue, visible red and visible infrared radiation. The combination of the three
bands is a modification of NDVI, with a soil adjustment factor and inclusion of the blue band for
reduction of atmospheric aerosol scattering produced by the red wavelength band (Jenson, 2000). The
modification was made to remove canopy background noise for the Moderate Resolution Imaging
- 72 -
Spectroradiometer (MODIS) platform on the Earth Observing Satellite (EOS) (Huete et al. 1997). MODIS
has similar band structure to those of the Landsat TM sensor and can be calibrated with Landsat TM
images for analysis, so the index was consequently used in this study. When used to assess forest types
around the world (ex. Siberian boreal forest, Amazon Basin tropical forests) using MODIS and Landsat
TM data, there was a strong positive correlation between field-measured near-infrared reflectance in
the forests and EVI value displayed in the images, but no relationship was found with measured red
reflectances (Huete et al. 1997). EVI also demonstrated a strong relationship with leaf area index (LAI) in
the forests (Huete et al. 1997).
- 73 -
4.4 Limitations of study
Accurate discrimination of individual mangroves to the species level is still extremely difficult,
even with the highest spatial and spectral resolution. Mixed pixels have been demonstrated as a primary
source of error in attempts to estimate the leaf-area-index of mangroves with NDVI using Landsat TM
data (Green et al. 1997). In attempt to estimate mangrove biomass images in The Guangdong Province
in South China using NDVI and Radarsat, it was concluded that Radarsat has advantages over the
Landsat TM index NDVI, due to the index’s overestimate of certain species and underestimate of others
(Li et al. 2007). In cases like this, error may arise due to high biomass or density of mangroves in an area,
which corroborates also with the dense mangroves evaluated in this study. Challenges like these limit
the accuracy of results when using 30m resolution data.
The tropics present a myriad of potential errors that must be considered and accounted for in
remote sensing studies. While high resolution remote sensing technology is invaluable in tropical and
coastal management, its application in quantitative analysis for ecology studies is still less than perfect.
The integration of high resolution, hyperspectral remote sensing technology with aerial photography
and GIS make optimizes analysis of forest stand characteristics (Dahdough-Guebas, 2002). For the scope
of this study, the cost for spatial data and timeline needed for this type of analysis were not available.
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5.0 CONCLUSIONS AND RECOMMENDATIONS
Roads constructed in forested areas have many consequences on the ecosystem. Fragmentation
of a forest can have consequences for wildlife continuity, hydrology aspects and reproductive
persistence of trees in the forest. In addition, constructed roads have consequences for land use
changes, especially in the tropics where mangroves are used for timber.
The results presented here are preliminary assessments for evidence of change in the dominant
species and overall biodiversity of mangroves. A new road is planned for the area, to make a connection
between Atasta and Palizada (Bach et al. 2005). The road would be placed through the wetlands
surrounding the Términos Lagoon, which is also fringed with mangroves (Bach et al. 2005).
Environmentalists in the area are concerned about habitat fragmentation, constricted hydrology
dynamics and increased urban traffic that the road would cause. This research may have greater insight
into how to test for and monitor large-scale changes in the species patterns and overall biodiversity of
mangroves using Landsat TM imagery. Sensors with higher resolution (Quickbird, SPOT, Ikonos, CASI)
can also be used to address these issues, as well as intensive empirical research concerning hydrology
dynamics surrounding roads.
For this study, the conclusions made based on the data provided by field analysis integrated
with multispectral satellite image analysis and GIS should be considered as preliminary findings that
suggest a general trend for the described impacts of the road. Empirical studies are encouraged to
substantiate the suggestions made by this study in order to quantify the degree of the described impacts
caused by the road.
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6.0 REFERENCES
Alongi D.M. 2002. Present state and future of the world’s mangrove forests. Environmental Conservation 29: 331-349.
Alongi, D.M. 2008. Mangrove forests: resilience, protection from tsunamis, and responses to global
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80
7.0 APPENDICES
Appendix A: Raw field data
Table 7.1 Abundance counts of mangrove in 30x30m sampling plots. Quadrat 5 was omitted from analysis due to inaccessibility for high density of mangroves.
Quadrat Easting (m)
Northing (m)
Longitude (degrees, minutes,
seconds)
Latitude (degrees, minutes,
seconds)
R. mangle A. germinans L. racemosa C. erectus L. Simpson’s index (1-D)
1 599280 2059050 -92°03’31.9337 18°37’12.2438 61 2 12 0 0.326 2 599310 2058870 -92°03’30.9422 18°37’06.3828 55 40 38 0 0.662 3 599430 2058630 -92°03’26.8905 18°36’58.5545 10 54 41 0 0.579 4 601530 2057550 -92°02’15.4317 18°36’23.0569 28 36 0 6 0.576 6 601410 2056950 -92°02’19.6357 18°36’03.5583 47 24 4 2 0.534 7 599880 2056170 -92°03’11.9781 18°35’38.4473 56 32 15 1 0.600 8 599790 2056050 -92°03’15.0702 18°35’34.5588 59 56 42 0 0.664 9 599910 2055840 -92°03’11.0139 18°35’27.7064 60 40 47 0 0.662
10 601050 2054340 -92°02’32.3930 18°34’38.7110 45 74 78 5 0.670 11 601170 2054850 -92°02’28.2065 18°34’55.2818 125 85 33 0 0.597 12 601620 2054490 -92°02’12.9205 18°34’43.4919 96 87 84 10 0.690 13 602040 2055630 -92°01’58.3832 18°35’20.5055 48 37 62 10 0.695 14 602400 2055570 -92°01’46.1123 18°35’18.4905 125 76 0 0 0.473 15 602578 2055510 -91°01’40.0506 18°35’16.5072 105 69 66 2 0.659 16 603270 2058480 -92°01’15.8900 18°36’53.0059 46 43 0 3 0.536 17 603480 2058570 -92°01’08.7077 18°36’55.8965 0 51 0 0 0.000
81
Appendix B: ANOVA results in curve fitting for candidates for suitable SVI
for predicting relative abundance of R. mangle. Analyses processed by SPSS Statistics 17.0. Selected SVI is highlighted.
Table 7.2 Results of curve fitting for TM4 prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .07587 1.14935 1 14 .30182 .121 .004 Logarithmic .06317 .94395 1 14 .34775 -.671 .256 Inverse .04957 .73018 1 14 .40721 .631 -
14.660
Quadratic .11681 .85966 2 13 .44603 1.161 -.027 .000 Cubic .11311 .82900 2 13 .45830 .800 -.011 .000 .000 Compound . . . . . .000 .000 Power . . . . . .000 .000 S . . . . . .000 .000 Growth . . . . . .000 .000 Exponential . . . . . .000 .000 Logistic . . . . . .000 .000
Table 7.3 Results of curve fitting for TM5 prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .03254 .47083 1 14 .50381 .587 -.006 Logarithmic .03748 .54521 1 14 .47248 1.016 -.184 Inverse .04254 .62199 1 14 .44346 .219 5.060 Quadratic .04787 .32683 2 13 .72696 1.178 -.051 .001 Cubic .04787 .32683 2 13 .72696 1.178 -.051 .001 .000 Compound . . . . . .000 .000 Power . . . . . .000 .000 S . . . . . .000 .000 Growth . . . . . .000 .000 Exponential . . . . . .000 .000 Logistic . . . . . .000 .000
Table 7.4 Results of curve fitting for TM7 prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .00002 .00032 1 14 .98607 .416 .000 Logarithmic .00049 .00693 1 14 .93482 .448 -
.017
Inverse .00146 .02048 1 14 .88824 .384 .217 Quadratic .01866 .12363 2 13 .88474 .809 -
.101 .006
Cubic .02295 .15268 2 13 .85992 .567 .000 -.007
.001
Compound . . . . . .000 .000 Power . . . . . .000 .000 S . . . . . .000 .000 Growth . . . . . .000 .000 Exponential . . . . . .000 .000 Logistic . . . . . .000 .000
82
Table 7.5 Results of curve fitting for NDVI prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .01209 .17129 1 14 .68524 .225 .289 Logarithmic .01211 .17161 1 14 .68496 .493 .184 Inverse .01228 .17402 1 14 .68289 .594 -
.117
Quadratic .01228 .08082 2 13 .92282 .433 -.367
.510
Cubic .01298 .08548 2 13 .91858 .456 .000 -.805
1.056
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.6 Results of curve fitting for SIPI prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .06508 .97450 1 14 .34032 .344 .252 Logarithmic
a . . . . . .000 .000
Inverse .01212 .17179 1 14 .68481 .411 -.002
Quadratic .08580 .61005 2 13 .55817 .342 -.040
.732
Cubic .09733 .43131 3 12 .73439 .303 -.275
3.072 -3.486
Compoundb . . . . . .000 .000
Powera,,b
. . . . . .000 .000 S
b . . . . . .000 .000
Growthb . . . . . .000 .000
Exponentialb . . . . . .000 .000
Logisticb . . . . . .000 .000
Table 7.7 Results of curve fitting for SR prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .01525 .21682 1 14 .64863 .320 .019 Logarithmic .01305 .18509 1 14 .67359 .278 .086 Inverse .01204 .17059 1 14 .68585 .496 -
.387
Quadratic .03124 .20962 2 13 .81358 .717 -.143
.015
Cubic .04943 .33799 2 13 .71929 .557 .000 -.021
.003
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
83
Table 7.8 Results of curve fitting for NDWI prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .32740 6.81488 1 14 .02055 -.389 1.841
Logarithmic .34010 7.21537 1 14 .01773 1.127 .851 Inverse .34940 7.51858 1 14 .01589 1.308 -.383 Quadratic .35212 3.53271 2 13 .05953 -1.848 8.291 -
6.987
Cubic .35212 3.53271 2 13 .05953 -1.848 8.291 -6.987
.000
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.9 Results of curve fitting for GVI prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .00532 .07493 1 14 .78828 .357 .012 Logarithmic .00357 .05018 1 14 .82599 .342 .048 Inverse .00205 .02881 1 14 .86764 .452 -
.166
Quadratic .01668 .11029 2 13 .89641 .735 -.149
.016
Cubic .01691 .11182 2 13 .89506 .507 .000 -.015
.002
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.10 Results of curve fitting for CVI prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .00520 .07317 1 14 .79072 .322 .041 Logarithmic .00216 .03029 1 14 .86433 .368 .056 Inverse .00038 .00537 1 14 .94260 .435 -.048 Quadratic .05643 .38876 2 13 .68552 1.632 -
1.179 .277
Cubic .05759 .39719 2 13 .68009 .829 .000 -.284
.087
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
84
Table 7.11 Results of curve fitting for MIRI prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .01569 .22318 1 14 .64391 .496 -.023 Logarithmic .01654 .23543 1 14 .63503 .529 -.094 Inverse .01987 .28380 1 14 .60258 .300 .377 Quadratic .01973 .13085 2 13 .87849 .342 .056 -.009 Cubic .49387 3.90312 3 12 .03704 7.336 -
5.450 1.358 -
.107 Compound
a . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.12 Results of curve fitting for GEMI prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .07353 1.11117 1 14 .30968 .298 .000 Logarithmic
a . . . . . .000 .000
Inverse .02377 .34092 1 14 .56859 .464 125.439 Quadratic .10728 .78111 2 13 .47825 .451 .000 .000 Cubic .12712 .58251 3 12 .63774 .196 .000 .000 .000 Compound
b . . . . . .000 .000
Powera,,b
. . . . . .000 .000 S
b . . . . . .000 .000
Growthb . . . . . .000 .000
Exponentialb . . . . . .000 .000
Logisticb . . . . . .000 .000
Table 7.13 Results of curve fitting for EVI prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .01360 .19303 1 14 .66711 .095 .147 Logarithmic .01495 .21254 1 14 .65186 .159 .331 Inverse .01649 .23470 1 14 .63555 .758 -.742 Quadratic .04002 .27096 2 13 .76685 -6.824 6.613 -
1.503
Cubic .04002 .27096 2 13 .76685 -6.824 6.613 -1.503
.000
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
85
Table 7.14 Results of curve fitting for TM4-TM2 prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .05862 .87185 1 14 .36627 .223 .004 Logarithmic .03927 .57225 1 14 .46191 -.133 .142 Inverse .02177 .31159 1 14 .58552 .508 -
4.314
Quadratic .12598 .93691 2 13 .41676 .881 -.026 .000 Cubic .12420 .92182 2 13 .42230 .679 -.012 .000 .000 Compound
a . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.15 Results of curve fitting for ARVI prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .13515 2.18774 1 14 .16126 .796 -.181
Logarithmic .12472 1.99490 1 14 .17967 .680 -.364
Inverse .11105 1.74888 1 14 .20722 .072 .697 Quadratic .14710 1.12107 2 13 .35550 .170 .421 -
.140
Cubic .14360 1.08993 2 13 .36508 .439 .078 .000 -.018
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.16 Results of curve fitting for TM4-TM3 prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .06094 .90852 1 14 .35668 .213 .004 Logarithmic .04767 .70075 1 14 .41659 -.249 .166 Inverse .03498 .50742 1 14 .48796 .547 -
6.945
Quadratic .10175 .73633 2 13 .49782 .803 -.019 .000 Cubic .10493 .76197 2 13 .48650 .489 .000 .000 .000 Compound
a . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
86
Table 7.17 Results of curve fitting for TM3-TM2 prediction of relative abundance of R. mangle.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .03079 .44482 1 14 .51565 .300 -.017
Logarithmica . . . . . .000 .000
Inverse .02900 .41818 1 14 .52831 .511 .596 Quadratic .04182 .28366 2 13 .75757 .551 .066 .006 Cubic .06128 .42433 2 13 .66295 .482 .000 -
.009 -
.001 Compound
b . . . . . .000 .000
Powera,,b
. . . . . .000 .000 S
b . . . . . .000 .000
Growthb . . . . . .000 .000
Exponentialb . . . . . .000 .000
Logisticb . . . . . .000 .000
87
Appendix C: ANOVA analysis in curve fitting for candidates for suitable
SVI for predicting relative abundance of A. germinans. Analyses processed by SPSS Statistics 17.0. Selected SVI is highlighted.
Table 7.18 Results of curve fitting for TM4 prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .20774 3.67091 1 14 .07602 .864 -.007 Logarithmic .21203 3.76714 1 14 .07268 2.384 -.475 Inverse .21240 3.77552 1 14 .07239 -.082 30.714 Quadratic .21096 1.73785 2 13 .21436 1.159 -.016 .000 Cubic .21096 1.73785 2 13 .21436 1.159 -.016 .000 .000 Compound .22822 4.13996 1 14 .06128 2.056 .973 Power .20796 3.67595 1 14 .07584 460.185 -1.724 S .18705 3.22124 1 14 .09430 -2.727 105.582 Growth .22822 4.13996 1 14 .06128 .721 -.027 Exponential .22822 4.13996 1 14 .06128 2.056 -.027 Logistic .22822 4.13996 1 14 .06128 .486 1.027
Table 7.19 Results of curve fitting for TM5 prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .00405 .05699 1 14 .81478 .437 -.002 Logarithmic .00415 .05841 1 14 .81253 .579 -.062 Inverse .00398 .05591 1 14 .81651 .315 1.566 Quadratic .00468 .03058 2 13 .96995 .558 -.011 .000 Cubic .00501 .03275 2 13 .96786 .539 -.008 .000 .000 Compound .00001 .00009 1 14 .99273 .312 1.000 Power .00034 .00482 1 14 .94563 .390 -.065 S .00149 .02084 1 14 .88726 -1.289 3.507 Growth .00001 .00009 1 14 .99273 -1.164 .000 Exponential .00001 .00009 1 14 .99273 .312 .000 Logistic .00001 .00009 1 14 .99273 3.202 1.000
Table 7.20 Results of curve fitting for TM7 prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .00354 .04978 1 14 .82668 .423 -.006 Logarithmic .00102 .01428 1 14 .90658 .426 -.025 Inverse .00000 .00006 1 14 .99392 .377 -.012 Quadratic .02408 .16037 2 13 .85349 .005 .101 -
.006
Cubic .01733 .11461 2 13 .89260 .188 .039 .000 .000 Compound .04761 .69979 1 14 .41691 .167 1.083 Power .07054 1.06256 1 14 .32011 .066 .761 S .09848 1.52937 1 14 .23655 -.276 -
6.607
Growth .04761 .69979 1 14 .41691 -1.792 .080 Exponential .04761 .69979 1 14 .41691 .167 .080 Logistic .04761 .69979 1 14 .41691 6.004 .923
88
Table 7.21 Results of curve fitting for NDVI prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .21662 3.87131 1 14 .06925 1.179 -1.238 Logarithmic .22868 4.15081 1 14 .06098 .020 -.810 Inverse .24056 4.43459 1 14 .05375 -.440 .522 Quadratic .26541 2.34853 2 13 .13467 4.518 -
11.765 8.188
Cubic .26541 2.34853 2 13 .13467 4.518 -11.765
8.188 .000
Compound .22283 4.01419 1 14 .06487 6.248 .010 Power .21432 3.81902 1 14 .07095 .089 -2.871 S .20603 3.63282 1 14 .07739 -3.917 1.770 Growth .22283 4.01419 1 14 .06487 1.832 -4.601 Exponential .22283 4.01419 1 14 .06487 6.248 -4.601 Logistic .22283 4.01419 1 14 .06487 .160 99.556
Table 7.22 Results of curve fitting for SIPI prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .27346 5.26954 1 14 .03767 .517 -.524 Logarithmic
a . . . . . .000 .000
Inverse .05916 .88035 1 14 .36401 .371 -.005 Quadratic .29356 2.70112 2 13 .10447 .514 -.816 .729 Cubic .33630 2.02686 3 12 .16389 .439 -
1.273 5.290 -
6.793 Compound .20593 3.63065 1 14 .07747 .494 .189 Power
a . . . . . .000 .000
S .02640 .37969 1 14 .54766 -1.166 -.011 Growth .20593 3.63065 1 14 .07747 -.705 -
1.665
Exponential .20593 3.63065 1 14 .07747 .494 -1.665
Logistic .20593 3.63065 1 14 .07747 2.024 5.286
Table 7.23 Results of curve fitting for SR prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .17948 3.06236 1 14 .10200 .695 -.064
Logarithmic .20197 3.54324 1 14 .08074 .911 -.341
Inverse .22576 4.08228 1 14 .06289 .010 1.698
Quadratic .20869 1.71426 2 13 .21839 1.238 -.286 .021
Cubic .20869 1.71426 2 13 .21839 1.238 -.286 .021 .000
Compound .26727 5.10675 1 14 .04030 1.320 .750
Power .24007 4.42279 1 14 .05403 2.677 -1.363
S .21588 3.85436 1 14 .06980 -2.463 6.081 Growth .26727 5.10675 1 14 .04030 .277 -.288 Exponential .26727 5.10675 1 14 .04030 1.320 -.288 Logistic .26727 5.10675 1 14 .04030 .758 1.334
89
Table 7.24 Results of curve fitting for NDWI prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .23845 4.38350 1 14 .05497 1.068 -1.590 Logarithmic .24683 4.58802 1 14 .05026 -.241 -.734 Inverse .25127 4.69823 1 14 .04792 -.394 .329 Quadratic .25835 2.26418 2 13 .14332 2.392 -7.448 6.345 Cubic .26024 2.28664 2 13 .14096 1.993 -4.668 .000 4.744 Compound .28924 5.69733 1 14 .03165 5.155 .002 Power .28379 5.54725 1 14 .03362 .028 -2.882 S .27307 5.25914 1 14 .03783 -4.090 1.256 Growth .28924 5.69733 1 14 .03165 1.640 -6.417 Exponential .28924 5.69733 1 14 .03165 5.155 -6.417 Logistic .28924 5.69733 1 14 .03165 .194 611.893
Table 7.25 Results of curve fitting for GVI prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .10848 1.70357 1 14 .21287 .628 -.057 Logarithmic .11883 1.88801 1 14 .19103 .789 -.283 Inverse .12721 2.04047 1 14 .17509 .061 1.318 Quadratic .12593 .93645 2 13 .41693 1.101 -.260 .020 Cubic .12593 .93645 2 13 .41693 1.101 -.260 .020 .000 Compound .24609 4.56993 1 14 .05066 1.269 .731 Power .22303 4.01868 1 14 .06474 2.511 -
1.419
S .19798 3.45588 1 14 .08417 -2.589 6.025 Growth .24609 4.56993 1 14 .05066 .239 -.313 Exponential .24609 4.56993 1 14 .05066 1.269 -.313 Logistic .24609 4.56993 1 14 .05066 .788 1.368
Table 7.26 Results of curve fitting for CVI prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .05383 .79657 1 14 .38720 .670 -.133 Logarithmic .05319 .78655 1 14 .39013 .597 -.282 Inverse .05052 .74490 1 14 .40264 .115 .563 Quadratic .05405 .37139 2 13 .69686 .756 -.213 .018 Cubic .05434 .37352 2 13 .69546 .762 -.197 .000 .004 Compound .02420 .34721 1 14 .56509 .651 .721 Power .02897 .41772 1 14 .52853 .573 -.763 S .03278 .47441 1 14 .50222 -1.923 1.662 Growth .02420 .34721 1 14 .56509 -.430 -.327 Exponential .02420 .34721 1 14 .56509 .651 -.327 Logistic .02420 .34721 1 14 .56509 1.537 1.386
Table 7.27 Results of curve fitting for MIRI prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .00175 .02461 1 14 .87759 .403 -.008 Logarithmic .00038 .00535 1 14 .94275 .393 -.015
90
Inverse .00018 .00251 1 14 .96074 .386 -.036 Quadratic .00470 .03072 2 13 .96982 .270 .060 -.008 Cubic .38110 2.46311 3 12 .11258 -6.037 5.025 -
1.241 .096
Compound .08010 1.21906 1 14 .28816 .632 .823 Power .07106 1.07098 1 14 .31827 .774 -.725 S .05386 .79697 1 14 .38709 -1.845 2.303 Growth .08010 1.21906 1 14 .28816 -.459 -.194 Exponential .08010 1.21906 1 14 .28816 .632 -.194 Logistic .08010 1.21906 1 14 .28816 1.583 1.215
Table 7.28 Results of curve fitting for GEMI prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .21297 3.78845 1 14 .07196 .572 .000 Logarithmic
a . . . . . .000 .000
Inverse .22649 4.09943 1 14 .06241 .213 -391.895 Quadratic .21448 1.77473 2 13 .20823 .605 .000 .000
Cubic .36816 2.33067 3 12 .12592 1.325 .001 .000 .000 Compound .28855 5.67804 1 14 .03190 .730 1.000 Power
a . . . . . .000 .000
S .16009 2.66840 1 14 .12464 -1.653 -1206.901
Growth .28855 5.67804 1 14 .03190 -.314 .000 Exponential .28855 5.67804 1 14 .03190 .730 .000 Logistic .28855 5.67804 1 14 .03190 1.369 1.000
Table 7.29 Results of curve fitting for EVI prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .19564 3.40523 1 14 .08624 1.595 -.565 Logarithmic .20591 3.63015 1 14 .07749 1.328 -1.243 Inverse .21663 3.87140 1 14 .06925 -.892 2.721 Quadratic .31536 2.99406 2 13 .08521 16.502 -
14.497 3.239
Cubic .31536 2.99406 2 13 .08521 16.502 -14.497
3.239 .000
Compound .18986 3.28097 1 14 .09159 25.702 .130 Power .18758 3.23248 1 14 .09378 8.812 -4.345 S .18541 3.18646 1 14 .09593 -5.449 9.220 Growth .18986 3.28097 1 14 .09159 3.247 -2.040 Exponential .18986 3.28097 1 14 .09159 25.702 -2.040 Logistic .18986 3.28097 1 14 .09159 .039 7.687
Table 7.30 Results of curve fitting for TM4-TM2 prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .21500 3.83431 1 14 .07045 .742 -.008 Logarithmic .21252 3.77822 1 14 .07230 1.661 -.334 Inverse .19883 3.47436 1 14 .08343 .082 13.195 Quadratic .21512 1.78153 2 13 .20712 .771 -.009 .000 Cubic .21512 1.78153 2 13 .20712 .771 -.009 .000 .000
91
Compound .23970 4.41386 1 14 .05424 1.303 .971 Power .20347 3.57624 1 14 .07949 31.586 -1.198 S .16649 2.79650 1 14 .11666 -2.136 44.230 Growth .23970 4.41386 1 14 .05424 .264 -.029 Exponential .23970 4.41386 1 14 .05424 1.303 -.029 Logistic .23970 4.41386 1 14 .05424 .768 1.030
Table 7.31 Results of curve fitting for ARVI prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .17533 2.97657 1 14 .10647 -.068 .209 Logarithmic .14914 2.45400 1 14 .13954 .079 .403 Inverse .12168 1.93943 1 14 .18545 .736 -.738 Quadratic .27325 2.44392 2 13 .12560 1.746 -1.537 .406 Cubic .26523 2.34631 2 13 .13489 1.111 -.647 .000 .060 Compound .11942 1.89855 1 14 .18987 .083 1.880 Power .10051 1.56442 1 14 .23152 .129 1.212 S .08014 1.21970 1 14 .28804 -.082 -2.194 Growth .11942 1.89855 1 14 .18987 -2.493 .631 Exponential .11942 1.89855 1 14 .18987 .083 .631 Logistic .11942 1.89855 1 14 .18987 12.096 .532
Table 7.32 Results of curve fitting for TM4-TM3 prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .22237 4.00340 1 14 .06519 .761 -.007 Logarithmic .22885 4.15472 1 14 .06087 1.843 -.369 Inverse .23057 4.19531 1 14 .05977 .027 18.047 Quadratic .22453 1.88200 2 13 .19150 .898 -.012 .000 Cubic .22453 1.88200 2 13 .19150 .898 -.012 .000 .000 Compound .25368 4.75878 1 14 .04669 1.426 .973 Power .21947 3.93653 1 14 .06721 60.913 -1.322 S .18796 3.24047 1 14 .09342 -2.307 59.689 Growth .25368 4.75878 1 14 .04669 .355 -.027 Exponential .25368 4.75878 1 14 .04669 1.426 -.027 Logistic .25368 4.75878 1 14 .04669 .701 1.028
Table 7.33 Results of curve fitting for TM3-TM2 prediction of relative abundance of A. germinans.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .10784 1.69219 1 14 .21432 .588 .031 Logarithmic
a . . . . . .000 .000
Inverse .09542 1.47677 1 14 .24438 .195 -1.095 Quadratic .11849 .87369 2 13 .44054 .338 -.051 -.006 Cubic .12646 .94095 2 13 .41529 .403 .000 .005 .001 Compound .14744 2.42110 1 14 .14202 .783 1.144 Power
a . . . . . .000 .000
S .08727 1.33855 1 14 .26665 -1.786 -3.836 Growth .14744 2.42110 1 14 .14202 -.244 .135 Exponential .14744 2.42110 1 14 .14202 .783 .135 Logistic .14744 2.42110 1 14 .14202 1.277 .874
92
Appendix D: ANOVA analysis in curve fitting for candidates for suitable
SVI for predicting Simpson’s biodiversity (1-D). Analyses processed by SPSS Statistics 17.0. Selected SVI is highlighted.
Table 7.34 Results of curve fitting for TM4 prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .03330 .48228 1 14 .49875 .385 .002 Logarithmic .04998 .73659 1 14 .40521 -.298 .202 Inverse .06743 1.01228 1 14 .33143 .783 -
15.179
Quadratic .21484 1.77862 2 13 .20759 -1.559 .061 .000 Cubic .21812 1.81327 2 13 .20203 -.957 .033 .000 .000 Compound
a . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.35 Results of curve fitting for TM5 prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .02950 .42553 1 14 .52476 .410 .005 Logarithmic .02495 .35825 1 14 .55904 .120 .133 Inverse .01982 .28305 1 14 .60305 .674 -
3.066
Quadratic .04118 .27917 2 13 .76083 .868 -.029 .001 Cubic .03923 .26541 2 13 .77095 .693 -.011 .000 .000 Compound
a . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.36 Results of curve fitting for TM7 prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .07985 1.21498 1 14 .28894 .359 .025 Logarithmic .08987 1.38242 1 14 .25930 .136 .206 Inverse .09387 1.45038 1 14 .24843 .763 -
1.545
Quadratic .11321 .82978 2 13 .45798 -.107 .144 -.007
Cubic .12087 .89369 2 13 .43285 .005 .093 .000 .000 Compound
a . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
93
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.37 Results of curve fitting for NDVI prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .09037 1.39095 1 14 .25790 .102 .702 Logarithmic .11189 1.76376 1 14 .20540 .775 .497 Inverse .13461 2.17770 1 14 .16216 1.092 -.342 Quadratic .46876 5.73542 2 13 .01638 -8.053 26.414 -
19.999
Cubic .47141 5.79695 2 13 .01586 -5.387 13.708 .000 -10.402
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.38 Results of curve fitting for SIPI prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .11787 1.87071 1 14 .19295 .476 .302 Logarithmic
a . . . . . .000 .000
Inverse .13266 2.14129 1 14 .16547 .563 .006 Quadratic .34189 3.37678 2 13 .06591 .482 1.157 -
2.136
Cubic .34511 2.10787 3 12 .15263 .501 1.267 -3.233
1.635
Compoundb . . . . . .000 .000
Powera,,b
. . . . . .000 .000 S
b . . . . . .000 .000
Growthb . . . . . .000 .000
Exponentialb . . . . . .000 .000
Logisticb . . . . . .000 .000
Table 7.39 Results of curve fitting for SR prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .02743 .39482 1 14 .53989 .447 .022 Logarithmic .06179 .92207 1 14 .35322 .297 .166 Inverse .10700 1.67747 1 14 .21621 .778 -
1.025
Quadratic .49310 6.32293 2 13 .01208 -1.454 .797 -.074
Cubic .49310 6.32293 2 13 .01208 -1.454 .797 -.074
.000
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
94
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.40 Results of curve fitting for NDWI prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .00590 .08313 1 14 .77733 .461 .219 Logarithmic .00943 .13321 1 14 .72059 .663 .126 Inverse .01358 .19271 1 14 .66737 .714 -.067 Quadratic .07099 .49671 2 13 .61962 -1.639 9.511 -
10.066
Cubic .07099 .49671 2 13 .61962 -1.639 9.511 -10.066
.000
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.41 Results of curve fitting for GVI prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .00212 .02980 1 14 .86541 .526 .007 Logarithmic .01243 .17620 1 14 .68103 .440 .080 Inverse .03057 .44151 1 14 .51719 .692 -
.567
Quadratic .30727 2.88313 2 13 .09198 -1.210 .751 -.075
Cubic .30278 2.82275 2 13 .09592 -.658 .393 .000 -.005
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.42 Results of curve fitting for CVI prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .02083 .29783 1 14 .59383 .396 .073 Logarithmic .01867 .26640 1 14 .61382 .442 .147 Inverse .01567 .22284 1 14 .64416 .684 -
.275
Quadratic .02241 .14902 2 13 .86301 .600 -.118
.043
Cubic .02241 .14902 2 13 .86301 .600 -.118
.043 .000
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
95
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.43 Results of curve fitting for MIRI prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .02287 .32769 1 14 .57610 .646 -.025
Logarithmic .02642 .37986 1 14 .54757 .688 -.106
Inverse .02916 .42057 1 14 .52715 .436 .406 Quadratic .02973 .19918 2 13 .82186 .824 -
.116 .011
Cubic .03189 .13177 3 12 .93930 .405 .214 -.071
.006
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.44 Results of curve fitting for GEMI prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .01872 .26705 1 14 .61338 .506 .000 Logarithmic
a . . . . . .000 .000
Inverse .12483 1.99691 1 14 .17947 .662 255.186 Quadratic .28075 2.53719 2 13 .11741 .126 .000 .000 Cubic .28129 1.56556 3 12 .24892 .089 .000 .000 .000 Compound
b . . . . . .000 .000
Powera,,b
. . . . . .000 .000 S
b . . . . . .000 .000
Growthb . . . . . .000 .000
Exponentialb . . . . . .000 .000
Logisticb . . . . . .000 .000
96
Table 7.45 Results of curve fitting for EVI prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .10295 1.60674 1 14 .22563 -.219 .360 Logarithmic .11861 1.88395 1 14 .19148 -.077 .827 Inverse .13540 2.19238 1 14 .16085 1.436 -1.887
Quadratic .63913 11.51211 2 13 .00133 -27.890 26.221 -6.012
Cubic .63913 11.51211 2 13 .00133 -27.890 26.221 -6.012
.000
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.46 Results of curve fitting for TM4-TM2 prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .03745 .54470 1 14 .47268 .423 .003 Logarithmic .06019 .89658 1 14 .35976 -.043 .156 Inverse .07842 1.19137 1 14 .29348 .718 -
7.268
Quadratic .20440 1.66995 2 13 .22621 -.496 .044 .000 Cubic .21841 1.81640 2 13 .20154 -.250 .026 .000 .000 Compound
a . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.47 Results of curve fitting for ARVI prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .03990 .58185 1 14 .45825 .742 -.087
Logarithmic .03213 .46474 1 14 .50654 .677 -.164
Inverse .02467 .35415 1 14 .56127 .415 .292 Quadratic .08292 .58774 2 13 .56969 -.312 .928 -
.236
Cubic .08054 .56936 2 13 .57938 .047 .417 .000 -.036
Compounda . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
97
Table 7.48 Results of curve fitting for TM4-TM3 prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .04084 .59615 1 14 .45289 .412 .003 Logarithmic .06955 1.04651 1 14 .32367 -.153 .178 Inverse .09952 1.54725 1 14 .23397 .758 -
10.400
Quadratic .27945 2.52088 2 13 .11880 -.854 .052 .000 Cubic .28210 2.55416 2 13 .11599 -.463 .028 .000 .000 Compound
a . . . . . .000 .000
Powera . . . . . .000 .000
Sa . . . . . .000 .000
Growtha . . . . . .000 .000
Exponentiala . . . . . .000 .000
Logistica . . . . . .000 .000
Table 7.49 Results of curve fitting for TM3-TM2 prediction of 1-D.
Equation
Model Summary Parameter Estimates
R Square F df1 df2 Sig. Constant b1 b2 b3
Linear .02933 .42309 1 14 .52593 .460 -.014
Logarithmica . . . . . .000 .000
Inverse .06905 1.03841 1 14 .32548 .691 .817 Quadratic .23628 2.01103 2 13 .17340 -.505 -
.332 -
.024
Cubic .24997 2.16634 2 13 .15417 -.212 -.183
.000 .001
Compoundb . . . . . .000 .000
Powera,,b
. . . . . .000 .000 S
b . . . . . .000 .000
Growthb . . . . . .000 .000
Exponentialb . . . . . .000 .000
Logisticb . . . . . .000 .000
