A Method for Using Global MODIS VCF Data to Evaluate Land ... · This Dissertation...

King’s College London, MSc Dissertation: Global Environmental Change Henry Brittlebank

KING’S COLLEGE LONDON

A Method for Using Global MODIS VCF Data to Evaluate Land-Cover and Land-Use Change in

Protected Areas Throughout the Western Ghats, India.

Henry Brittlebank

8/30/2013

This dissertation is submitted as part of a MSc degree in Global Environmental Change at King’s College London.


KING’S COLLEGE LONDON

UNIVERSITY OF LONDON

DEPARTMENT OF GEOGRAPHY

MA/MSc DISSERTATION

I, ………Henry Brittlebank…………………

hereby declare (a) that this Dissertation is my own original

work and that all source material used is acknowledged

therein; (b) that it has been specially prepared for a degree of

the University of London; and (c) that it does not contain any

material that has been or will be submitted to the Examiners of

this or any other university, or any material that has been or

will be submitted for any other examination.

This Dissertation ………………11,888………………words.

Signed: …………………………………………...…………….

Date: …………….30/08/13………………………………….


Abstract

Post-classification analysis of Landsat data provides evidence of deforestation, with increases in

SWG and Agriculture at the expense of Dense Forest land cover in Mudumalai NP and increases of

Degraded Forest cover in Silent Valley NP, 1973 – 2012. Landscape characterisation of Mudumalai

and Silent Valley NP was then achieved using Global MODIS VCF data aided by high-resolution

Landsat classifications, with classification accuracies of 76.47% and 84.62% respectively. LUCC

detection between 2000 - 2008 using MODIS VCF data also showed trends of deforestation with

increases of SWG, Agriculture and Degraded Forest cover in the National Parks. The new

methodology using Global MODIS VCF data was then applied to map land-cover of 27 PAs in the WG

biodiversity hotspot. LUCC detection from VCF data provides evidence of afforestation throughout

PAs in the WG of 768,351,070.60 m2 and PAs in the NBR of 208,699,048.03 m2 with decreases in

SWG and Agriculture land-cover.

Acknowledgments I would like thank my dissertation supervisor and course tutor Nick Drake for all his support and

advice on this research project and during my Masters at King’s College London. I would also like to

thank the Geography department at King’s for their assistance over the year.

Finally I would like to thank my parents, friends and GH for all their support throughout, I could not

have done it without them.


List of Abbreviations and Acronyms

WG - Western Ghats

NBR – Nilgiri Biosphere Reserve

PA – Protected Area

NP – National park

WS – Wildlife Sanctuary

TR – Tiger Reserve

RF – Reserve Forest

FR – Forest Range

LUCC – Land Use and Cover Change

SWG – Sparsely Wooded Grassland

NWFP – Non-Wood Forest Product

GLCD – Global Land Cover Data

IUCN – International Union for Conservation of Nature

mm – Millimetres

m – Metres

km – Kilometres

µm – Micrometre

C – Celsius

DOS – Dark Object Subtraction

Lλ – Spectral Radiance

λ - Wavelength

Key Words

Land-Use, Afforestation, Protected Area, Western Ghats, MODIS VCF


Table of Contents 1. Introduction .................................................................................................................................... 1

2. Literature Review ........................................................................................................................... 2

2.1. Global Land Use and Land Cover Change ............................................................................... 2

2.2. Advances in Remote Sensing of Land Use and Land Cover Change ....................................... 5

2.3. Land Use and Land Cover Change: A Case Study of India ....................................................... 7

2.4. Land Use and Land Cover Change in the Western Ghats, India ............................................. 9

2.5. LUCC in Protected Areas of the Western Ghats and the Nilgiri Biosphere Reserve. ............ 11 2.6 Research Questions and Hypotheses ………………………………………………………………………………14

3. Study Area .................................................................................................................................... 14

3.1. Western Ghats and the Nilgiri Biosphere Reserve ................................................................ 14

3.2. Silent Valley National Park .................................................................................................... 15

3.3. Mudumalai National Park and Wildlife Sanctuary ................................................................ 16

4. Data ............................................................................................................................................... 19

4.1. Landsat Data ......................................................................................................................... 19

4.1.1. Pre-processing and Scene Selection ............................................................................. 20

4.1.2. Reflectance Values from Digital Numbers .................................................................... 20

4.1.3. Data Concatenation and Layer Stacking ....................................................................... 21

4.1.4. Geo-registration of Landsat MSS Data ....................................................................... 21

4.1.5. Atmospheric Correction ................................................................................................ 22

4.1.6. Landsat 7 ETM+ Scan-Line Correction Failure and Gap-Filling ............................... 23

4.2. Classification ........................................................................................................................ 23

4.2.1. National Park Boundaries and Image Masking ......................................................... 23

4.2.2. Regions of Interest and Transform Divergence ........................................................ 24

4.2.3. Classification and Post Classification ......................................................................... 25

4.2.4. Accuracy Assessment .................................................................................................. 25

4.3. MODIS Vegetation Continuous Field (VCF) Data .............................................................. 26

4.3.1. Generating regional MODIS VCF 250m Land-Cover Maps ....................................... 27

4.3.2. VCF Land-Cover Maps Across the Western Ghats..................................................... 28

15 15


5. Results ........................................................................................................................................... 29

5.1. Landsat 1973 – 2012Mudumalai National Park Maximum Liklehood Classified Data ......... 29

5.2. Landsat 1973 – 2012 Silent Valley National Park Maximum Liklehood Classified Data ....... 33

5.3. Accuracy Assesment – Confusion Matricies of Landsat MLC Images ................................... 36

5.4 MODIS VCF Classified Data of Mudumalai National Park, 2000 - 2008 ................................ 37

5.5. MODIS VCF Classified Data of Silent Valley National Park, 2000 – 2008. ............................. 38

5.6. LUCC In National Parks and Wildlife Sanctuaries Across the Western Ghats Between 2000 - 2008, Based on MODIS VCF Data .................................................................................................... 40

5.7. LUCC In National Parks and Wildlife Sanctuaries Across the Nilgiri Biosphere Reserve Between 2000 - 2008, Based on MODIS VCF Data ........................................................................... 43

5.8. Accuracy Assesment – Confusion Matricies of MODIS VCF MLC Images ............................. 45

6. Discussion ..................................................................................................................................... 46

6.1. LUCC in Mudumalai National Park ........................................................................................ 46

6.2. LUCC in Silent Valley National Park ....................................................................................... 47

6.3. LUCC in the Buffer Zones of Mudumalai and Silent Valley National Park ............................ 48

6.4. MODIS VCF Classified Data of Mudumalai and Silent Valley National Park ......................... 49

6.5. LUCC in PAs of the Western Ghats and Nilgiri Biosphere Reserve using MODIS VCF GLCD. 50

7. Project Limitations and Future Research .................................................................................... 52 7.1. Project Limitations................................................................................................................52 7.2. Future Research....................................................................................................................52

8. Conclusion .................................................................................................................................... 53

References…………………………………………………………………………………………………………………………………….....i Appendix A………………………………………………………………………………………………………………………………………xii


Figure 2-0: Definitions of land-use and land-cover………………………………………………………………………….2 Figure 2-1: Transitions of Land-Use. ................................................................................................... 3 Figure 2-2: Worldwide Extent of Land-Cover. .................................................................................... 4 Figure 2-3: Global MODIS VCF Product - Global % Tree Cover. .......................................................... 6 Figure 2-4: The Western Ghats and Sri Lanka Biodiversity Hotspot. .................................................. 9 Figure 2-5: IUCN Category Status’s ................................................................................................... 11 Figure 2-6: Panel a) Existing Protected Areas and Site Irreplaceabilities in the Western Ghats. ..... 13 Figure 2-7: PAs in Nilgiri Biosphere Reserve………………………………………………………………………………….14 Figure 3-1: Protected Areas in Kerala and Tamil Nadu, India.. ......................................................... 16 Figure 4-1: Landsat 2003 False Colour Composite…………………………………………………………………………21 Figure 4-2: Landsat Geo-Referenced Image of Silent Valley National Park ……………………………………22 Figure 4-3: Landsat Mudumalai National Park……………………………………………………………………………….24 Figure 4-4: Ground Truth ROIs…………………………………………………………………………………….…………………26 Figure 4-5: MODIS VCF 2008 Mudumalai National Park…………………………………………………………………27 Figure 5-1: Landsat MLC Images of Mudumalai National Park 1973 – 2012.………………………………….30 Figure 5-2: Landsat MLC Images of Mudumalai National Park and 5km Buffer Zone.…………………….31 Figure 5-3: Graph of Landsat Class Data, Mudumalai National Park………………………………………….…..32 Figure 5-4: Landsat MLC, Silent Valley National Park……………………………………………………………….…….33 Figure 5-5: Landsat MLC, Silent Valley National Park and 5km Buffer Zone…………………………….…….34 Figure 5-6: Graph of Landsat Class Data, Silent Valley National Park……………………………………….…….35 Figure 5-7: Landsat MLC Accuracy Assessments.……………………………………………………………….... ………36 Figure 5-8: MODIS VCF Classifications, Mudumalai National Park……………………………………… …………37 Figure 5-9: MODIS VCF Classifications, Silent Valley National Park……………………………………… ……….38 Figure 5-10: MODIS VCF Classifications, Protected Areas in the Western Ghats…… …………….……….40 Figure 5-11: Graph of MODIS VCF Classification Data for Protected Areas in the Western Ghats ..41 Figure 5-12: MODIS VCF Classifications, Protected Areas in the NBR…………………………………………….43 Figure 5-13: Graph of MODIS VCF Classification Data for Protected Areas in the NBR…………………..44 Figure 5-14: MODIS VCF Accuracy Assessments…………………………………………………………………………….45

Table 2-1: Species Richness and Endemicity in the Western Ghats and Sri Lanka ............................. 10 Table 2-2: Land-Use and Deforestation rates in the southern region of the Western Ghats ............ 10 Table 2-3: Protected Areas in the Western Ghats and Sri Lanka. ....................................................... 12 Table 3-1: Protected Areas in the Western Ghats .............................................................................. 18 Table 4-1: Landsat Data used in the Study.........................................................................................20 Table 5-1: Landsat Class Area Data for Mudumalai National Park ..................................................... 32 Table 5-2: Landsat Class Area Data for Silent Valley National Park .................................................... 34 Table 5-3: MODIS VCF Class Area Data for Mudumalai National Park………………………………………………37 Table 5-4: MODIS VCF Class Area Data for Silent Valley National Park…………………………………………....38 Table 5-5: MODIS VCF Class Area Data for Protected Areas in the Western Ghats……………………..…..41 Table 5-6: MODIS VCF Class Area Data for Protected Areas in the NBR……………………………………………43 Equation 1: Conversion of Digital Numbers to Ground Surface Reflectance Values……………………….20 Equation 2: Chosen Digital Number Value used for Dark Object Subtraction…………………………………23

List of Figures

List of Equations

List of Tables


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1. Introduction

The Western Ghats (WG) mountain range, spanning 1600km along India’s western coastline, is one

of the world’s 34 biodiversity hotspots and is a designated UNESCO World Heritage Site. In the up-

hill battle to preserve increasingly threatened species and habitats, 34 hotspots were demarcated as

areas deserving a high priority for conservation. Forest and natural vegetation cover in the WG

provide habitats for numerous endemic flora and fauna as-well as endangered species including the

Asian Elephant, Bengal Tiger and Lion-tailed Macaque. With such high levels of species richness,

endemicity and biodiversity, the WG is an ecologically sensitive region of global importance

(UNESCO 2013.a).

Despite the fact that there are 88 Protected Areas (PA) covering over 9% of the WG (Gunawardene

et.al 2007) human activities and land-use, mainly in the form of agriculture, plantations, Marijuana

cultivation, livestock grazing, forest fire and collection of firewood and Non-Wood Forest Products

(NWFP), are resulting in deforestation throughout the region. Human activities and land-use

encroaching into National Parks and Wildlife Sanctuaries is one of the biggest problems facing PAs in

India and is of serious concern for conservation efforts in the WG. Landscape characterisation and

monitoring are important (especially for PAs) to studies concerning habitat and biodiversity,

management of forest resources and human-activity, human livelihoods and biogeochemical and

climate cycles (Hansen et.al 2007). There is a current lack of quantitative data and knowledge on the

changing land-use patterns and processes (Semwal et.al 2004) in PAs throughout the WG. There is a

need for a large-scale, quantitative methodology to provide data on landscape characterisation and

LUCC to improve and aid future conservation planning and management of PAs in the WG.


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2. Literature Review

2.1. Global Land Use and Land Cover Change

Alterations and contemporary change of the Earth’s land surface and vegetation cover are

increasingly being dominated by human activities. Human actions are currently altering the

terrestrial environment at unprecedented rates, magnitudes and spatial scales (Lambin et.al 2001).

Current change in many realms of the biosphere are largely the product of human activities, the

sources of which lie in two clusters of production and consumptions activities (Meyer & Turner

1994); 1) The world’s industrial metabolism, flow of energy and materials through processes of

resource extraction, processing, use and disposal. 2) Global Land-Use and Land-Cover Change

(Turner 1994).

Land-use and land-cover change (LUCC) stemming from human land-users, represents a major

source of global environmental change (Lambin et.al 2001). Patterns of landscape modification are

the results of complex interactions between physical, biological and social forces (Petit et.al 2001)

that involve site-specific interactions and a large number of factors at different spatial and temporal

scales (Lambin et.al 2003). However, settlement and urban extension, agriculture and plantation

expansion and wood extraction are considered the main, proximate causes (figure 1.1. Humans have

only recently been recognised as a dominant force in global environmental change (Turner 2003)

with LUCC being seen as an important facet only in the last few decades (Vitousek et.al 1997). While

research suggests that humans have been altering land-cover since pre-historic times, through

methods such as fire to flush out game and clearance of small patches for agriculture and livestock

(De Sherbinin 2002) it is really only in the past few centuries that humans have had the technology

to impact land-use and land-cover on a truly global scale (figure 2.1).

Land-Use – is the term used to describe human uses of the land, or activities, arrangements and

impacts people have undertaken on a land-cover type to produce, change or maintain it

Land-Cover – refers to the natural vegetative cover types that are characteristic of a particular area,

defined as the observed bio-physical cover on the Earth’s terrestrial surface.

Figure 2.0: Definitions of land-use and land-cover (Sherbinin 2002, Herold et.al 2009).


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The land-cover of the Earth’s surface has numerous complex interactions with the planet’s

biosphere. Changes to land-cover, in the form of changing land-use, can greatly impact and cause

large scale changes to the states and flows of the biosphere. LUCC directly impacts biotic diversity

worldwide (Sala et.al 2000) contributes to local and regional climate change (Chase et.al 2000) as

well as global warming, soil degradation and, by altering ecosystem services, affects the ability of

biological systems to support human needs (Lambin et.al 2003). Land-cover change, especially the

conversion of forested areas into others uses, account for 33% of the increase in atmospheric CO2

since 1850 (De Sherbinin 2002). Recent research on LUCC have revealed evidence on the on-going

processes of deforestation, land degradation, urbanisation and regional changes to biodiversity

(Serneels et.al 2007, Wessels et.al 2004, Wessels et.al 2007, Reidsma et.al 2006, Rudel et.al 2005).

When these impacts are aggregated it is easy to see why LUCC is considered one of the most

important contemporary facets of global environmental change.

Figure 2-1: Transitions of land-use which may be experienced within a given region over time, from a fully natural ecosystem to almost completely human managed land-use (Foley et.al 2005).


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Changes in land-cover occur mainly from changes in land-use which leads to a modification in the

bio-physical cover on the Earth’s surface (figure 2.2). In fact land-cover is constantly being

transformed by land-use changes. To understand the changing impacts of land-cover on the states

and flows of the biosphere, accurate up-to-date data and information on LUCC must first be

achieved. Reliable land-cover information is of crucial importance to understanding and mitigating

climate change, sustainable development, natural resource management and biodiversity

conservation, as well as understanding the on-going processes of deforestation, desertification,

urbanisation, ecosystem functions and water and energy management (Herold et.al 2009).

Figure 2-2: Worldwide extent of land-cover (top) and land-use change of croplands (middle) and pastures (bottom) in the 1990s (Foley et.al 2005).


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2.2. Advances in Remote Sensing of Land Use and Land Cover Change

Because LUCC is the result of numerous complex interactions over different spatial and temporal

scales, it is very difficult to accurately measure and observe. Ground observations can provide

accurate data on land-cover but cannot be used extensively over large areas to map change over

time, due to cost and time limitations. Remote sensing has emerged as the most useful data source

for quantitatively measuring land-cover changes and can provide precise and accurate LUCC analysis

through temporal series of satellite data (Petit et.al 2001). Satellite data provides the resources to

analyse multi-temporal land-cover data over large spatial areas. When analysing LUCC a remote

sensing approach allows for: 1) Quantification of land-cover changes in terms of percentage of area

affected and rates of change; 2) To qualify the nature of changes in terms of impact on natural

vegetation; 3) To map the spatial pattern of land-cover change (Petit et.al 2001). Recent advances in

remote sensing technology has allowed for an increase in accuracy in land-cover and use detection,

as well as improved efficiency providing and ‘economical’ means to map and analyse LUCC (Yuan

2005).

A majority of early work using remote sensing techniques analysed temporal series of multiple

spectral data to evaluate land-cover change. Numerous change detection methods have been

created, including; image differencing, vegetative index differencing, selective principle components

analysis and post classification change differencing (Mas 1999). Post classification methods are

currently the most widely used technique and are considered the most accurate procedures while

also indicating the nature of the changes (Petit 2001, DeFries et.al 2000). Post-classification analysis

of multi temporal data sets provide relatively detailed information and statistics on LUCC, including

the location, nature and rate of change.

Along with the developing understanding that land-cover change impacts global processes, remote

sensing land-cover change detection capabilities have evolved to allow the application of

classification models at regional, continental and global scales (Muchoney et.al 2000). In the past

decade data sources and methodologies for creating global land-cover maps from remote sensing

have evolved rapidly (Friedl et.al 2010). A majority of early global land-cover maps have used high

resolution data sets to interpret a coarse-resolution data set, which can then be used in a division

tree structure to classify global data sets into a range of pre-defined classes (Hansen et.al 2000).

Some of the most developed global land-cover datasets (GLCD) include: a 1km resolution GLCD from


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the International Geosphere Biosphere Programme – Data Information Systems (IGBD-DIS) trained

using 1km AVHRR data (Loveland et.al 2000); GLCD Global Land Cover 2000, co-ordinated by the

EU’s Joint Research Centre based on daily data from the VEGETATION sensor on-board SPOT-4

(Bartholomé & Belward 2005); a MODIS GLCD based on 1km AVHRR data (Hansen et.al 2000); and ,

more recently, the MODIS VCF product derived from all 7 bands of the MODIS sensor. MODIS VCF

data (figure 2.3) contains proportional estimates for vegetative cover types including; woody

vegetation, herbaceous vegetation and bare ground, allowing for better classification of

heterogeneous land-cover than discrete classification schemes (GLCF 2013). The VCF product has

been designed to continuously represent the Earth’s terrestrial surface as a proportion of basic

vegetation traits and provides continuous and quantitative data on land surface cover (USGS

2013.b).

Despite improvements in land-cover classification made possible by earth observing satellites, global

and regional land covers and uses are still relatively poorly enumerated (Loveland et.al 2000).

Vegetation diversity, interspersion and homogenous characteristics provide problems for remote

sensing classification (Friedl & Brodley 1997, Carpenter et.al 1997). Pixel mixing arises in

heterogeneous ground surfaces as the materials are so closely associated, and the complexity of the

relation between at surface spectral reflectance and land-cover attributes (Wang et.al 2007)

Figure 2-3: MODIS VCF product showing global % tree cover representing the Earth’s land surface as a proportion of basic vegetation traits (USGS 2013.b).


7

negatively impact the accuracy and usefulness of global land-cover products. There is an obvious

need for good inventory data and statistics on land-cover and LUCC at sub-national, national and

global scales (Lepers et.al 2005) which requires increased accuracy of land-cover characteristics as

well as better methods for the analysis of global land-cover data sets LUCC detection.

2.3. Land Use and Land Cover Change: A Case Study of India

Pre-colonial India was mainly a nation of people who relied on their immediate surroundings for

resources with a variety of cultural practises and prudent resource use (Gadgil et.al 1993). After

British colonisation (Raj rule - 1858] there was an exponential increase in resource exploitation and

deforestation leading to LUCC (Richards & Tucker 1988). Under British rule huge amounts of Indian

land was acquired as state property, resources were exported out of the locality, new agricultural

practises and improved transport links such as railways massively increased agricultural output and a

new economy was brought to India (Guha & Gadgil 1989, Sivaramakrishnan 1995). Colonialism,

modernity and development were not exclusively responsible for the land degradation in India

(Sinha et.al 1997) but early years of British rule where characterised by completely unregulated

exploitation of environmental services and resources, with the process of non-sustainable forest use

being intensified after independence, resulting in the contemporary large scale increase in land

degradation and LUCC throughout India (Gadgil et.al 1993).

India’s forests and biodiverse natural environments, mainly tropical and sub-tropical in nature,

constitute 64m hectares and such vegetation cover is predominant in the four major ecological

zones; Himalayas, Vindhyans, Eastern and Western Ghats (NRSA 2005). The wide-ranging and varied

vegetation cover with such a high biodiversity is the product of monsoon regimes, high precipitation

and humidity levels as well as spatial variability in climate. In the past few decades natural

environments and forested landscapes have been over exploited in the name of development, for

mineral resources, real estate interests and more recently tourism (Giriraj et.al 2011). Recent

research on land degradation and land-cover change in India has estimated an increase of over 263%

in timber requirements to 181,270 million tonnes by 2025, fuel wood stands (the main energy

resource for 70% of the Indian population) cause 125 million tonnes to be extracted annually and

over half the livestock population (270m) depend on forest for grazing resources (NRSA 2005). The

ministry of Environment and Forest in 1999 classified 43% of India’s 329m hectares of land as under

cropping while 23% were classified as forests. A majority of these forests however, have significantly

been disturbed through activities such as logging, clear-felling, grazing, fire and collection of fuel


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wood, fodder and NWFPs (Bhat et.al 2001). There is a consensus in current literature that British

colonisation increased resource exploitation which Independent India has expanded to cause rapid

land degradation, deforestation and LUCC in the 20th Century.

While a majority of research focuses on resource exploitation and LUCC from natural vegetation

cover to human land-uses, recent research has suggested that there has been an increase in forest

cover throughout India in the past few decades. Large scale plantation and rehabilitation

programmes undertaken in the recent past allow for natural regeneration and have resulted in an

increase in the area of rehabilitated secondary forests (Bhat et.al 2001). Recent research by Rudel

et.al (2005) suggest that the loss of forests during agricultural expansion creates a countervailing

tendency, continued decreases in forest cover spur increases in the prices of forest products, and

the price increases induce land owners to plant trees, thereby re-foresting areas, explaining the

recent trend of increasing forest cover in India. Data analysis of national household survey data,

census data and satellite images of land-use in rural India provide evidence that increases in the

demand for forest products associated with income and population growth has led to forest growth

in the past few decades (Foster & Rosenzwig 2003).

While a majority of research (Richards & Tucker 1988, Sivaramakrishnan 1995, Sinha et.al 1997,

Gadgil et.al 1993) suggests that India has been experiencing multiple decades of resource

exploitation and land degradation, recent research (Rudel et.al 2005, Foster & Rosenzwig 2003, Bhat

et.al 2001) provides evidence of forest growth in the past few decades. Semwal et.al (2004) implies

that knowledge of recent changes in land-use in India, driving forces and implications of change are

limited. According to a survey on LUCC in India, by the Indian Government (NRSA 2005), information

on LUCC in the form of quantitative data are inadequate and do not provide up-to-date information

on the changing land-use patterns and processes. This supports Pandit et.al (2007) who suggests

that inaccurate reporting of forest cover by governments can result in underestimates of the rates

and biological impacts of deforestation and land-cover change. While there is consensus on LUCC

and degradation up until and a few decades after Indian Independence, there is little consensus or

agreement on forest cover and LUCC in India during the last few decades and into the 21st Century.


9

2.4. Land Use and Land Cover Change in the Western Ghats, India

The Western Ghats, older than the Himalaya Mountains, represents a geomorphic region of

immense importance with unique biophysical and ecological processes, an exceptionally high level of

biological diversity and endemism, and is one of the world’s eight ‘hottest hotspots’ (UNESCO

2013.a) (figure 2.4). The WG is officially one of the 34 biodiversity hotspots (Malcolm et.al 2006)

combined with the vegetation and biodiversity of Sri Lanka. The original total combined primary

vegetation cover of the region was 182,500 km2 (Gunawardene et.al 2007) however limited biotic

interchange between these areas has created two distinct flora and fauna endemic areas (Bossuyt

et.al 2004) (table 2.1). The WG flora is largely influenced by the Indian Monsoon and the abundance

and distribution of precipitation this brings. The high Montane Forest ecosystems influence the

Indian Monsoon, this moderates the tropical climate of the region and provides on of the best

examples of the monsoon system on the planet (UNESCO 2013.a). The western side of the WG is on

the threshold of the south west monsoon and receives a rainfall of 203 – 254 cm, while the eastern

side lies in the rain shadow of the Peninsula creating many diverse soil types.Despite not being as

well known for its biodiversity as the Tropical Andes, Mediterranean Basin or the forests of

Madagascar, the WG with its endemic flora and fauna (table 2.1), high biodiversity and forest cover

is an ecologically sensitive region of global importance.

Figure 2-4: A map of India with the Western Ghats and Sri Lanka biodiversity hotspot highlighted (Conservation International 2013.a).


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While the original extent of the WG biodiversity hotspot was 182,500 km2 of primary vegetation,

research suggests only 12,450 km2 (6.8%) remains (Gunawardene et.al 2007). This is an estimated

change of disruption to natural vegetation cover of 93%, a drastic decrease throughout the region

and rapid forest loss. Many areas of the WG Mountain Range are inaccessible, hard to cultivate and

have such dense forest cover that such forest loss and land-cover change is hard to comprehend.

However, over the last few decades, due to indiscriminate and unscientific exploitation of these

forests, particularly for agriculture, construction of hydro-electric projects, raising monoculture

plantations and other activities, huge areas of the region have been disrupted and felled

(Chandrashekara & Ramakrishnan 1994). Research by Jha et.al (2000) covering the southern stretch

of the WG (approximately 40,000 km2) provides evidence of forest cover loss of 2279 km2,

amounting to 25.6% of total forest area, with an annual rate of deforestation at 1.16% from 1973 –

1995 (table 2.2).

Table 2-2: Changes in land-use and deforestation rates in the southern region of the Western Ghats, 1973 – 1995 (Jha et.al 2000).

Table 2-1: A overview and comparison of species richness and endemicity in the Western Ghats and Sri Lanka (Gunawardene et.al 2007).


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With continued increases of resource requirement, increased population pressures and continued

LUCC, more and more natural virgin forests are being encroached upon. The disruption of natural

vegetated land-cover in the WG has also led to an increase in land degradation from water induced

soil erosion (Singh et.al 1992). The mountainous geomorphology, intense precipitation levels during

monsoon season, decreasing rates of forest cover and increasing use of irrigation for agriculture all

add to increases of soil erosion causing land degradation. Despite numerous research and analysis of

general LUCC for the whole of the WG, there is actually very little quantitative data on LUCC, the

rate of such loss and spatial patterns of LUCC (Giriraj et.al 2011). The natural ecosystems of this

biodiversity hotspot are however clearly under threat and require urgent conservation action,

especially in augmenting the PA network (Gunawardene et.al 2007).

2.5. LUCC in Protected Areas of the Western Ghats and the Nilgiri Biosphere Reserve.

20 of the planet’s 34 biodiversity hotspots lie in tropical countries, areas that face the gravest

threats to their natural resources and have the most limited resources for conservation (Das et.al

2006). The increasing demand for natural resources in these areas combined with the lack of

resources for conservation, puts huge pressure on the natural environments, habitats, flora and

fauna in tropical biological hotspots. PAs cover around 5% of the total land area in India and support

4.5m people (Karanth et.al 2006), PA’s in the WG cover 9% of the land area (figure 2.6), which

includes 20 National Parks, 68 Sanctuaries (incorporating Wildlife Sanctuaries, Reserve Forests,

Forest Ranges and Tiger Reserves) and the Nilgiri Biosphere Reserve (NBR) (table 2.3, figure 2.6). The

20 NPs in the WG have IUCN Category II Status, while the WS throughout the region mainly have

IUCN Category IV Status (figure 2.5).

IUCN Category II:

National Park: protected area managed mainly for ecosystem protection and recreation. Natural area of land and/or sea, designated to a) protect the ecological integrity of one or more ecosystems for present and future generations, b) exclude exploitation or occupation inimical to the purposes of designation of the area and c) provide a foundation for spiritual, scientific, educational, recreational and visitor opportunities, all

of which must be environmentally and culturally compatible. IUCN Category IV:

Habitat/species Management Area: protected area managed mainly for conservation through management intervention. Are of land and/or sea subject to active intervention for management purposes so as to have the maintenance of habitats and/or to meet the requirements of specific species.

Figure 2-5: Definition of IUCN Category Status’s for National Parks (II) and Wildlife Sanctuaries (IV) from the IUCN ‘List of Protected Areas (Chape et.al 2003)


12

All PAs in the WG region aim to protect the land in its natural state, minimise and control human

activities and impacts on the environment, while maintaining and preserving high levels of

biodiversity and protecting endemic species. While the level of PAs in the WG is above average

(table 2.3) when compared with India as a whole, Ramesh et.al (1997) suggests that PAs in this

region have often been demarcated using ad hoc criteria, with many critical habitats such as lowland

dipterocarp dominated evergreen forests and Myristica swamps not being adequately represented.

Research by Das et.al (2006) into the coverage of conservation areas in WG (figure 2.6) provides

evidence that PAs are not representative in regards to numerous habitats and wildlife, including:

amphibians, endemic tree species and small mammals.

While PAs play an important role in conservation, they do not completely prevent human activities

impacting on the protected habitats and affecting land-cover. A survey of 58 PAs in the WG found

hunting, timber felling, presence of exotic and invasive species, extraction of firewood/fodder,

livestock grazing and fire were the most proximate threats to biodiversity (Gunawardene et.al 2007).

Somanathan & Borges (2000) implies that human activities have and are continuing to alter land-

cover and introducing new land-uses in many Indian PAs. While there has been research on LUCC

and deforestation in the WG as a whole (Jha et.al 2000, Chandrashekara & Ramakrishnan 1994,

Ramesh et.al 1997) there is very little research on LUCC throughout PAs in the region. Some research

(Inman 2011, Prakasam 2010) provides LUCC or land-cover data on individual parks, but these

techniques have not been expanded to map LUCC across all the PAs in the biodiversity hotspot.

Methods to quantify and monitor human induced forest disturbance are essential when observing

LUCC in PAs, developing buffer zones and managing human activities while forest communities

continue to live in and around PAs and derive livelihoods dependent on forest products, fuel wood

and livestock grazing (Karanth et.al 2006).

Table 2-3: Area and percentage of protected areas in the biodiversity hotspot of the Western Ghats and Sri Lanka regions (Gunawardene et.al 2007).


13

While NP’s, WS’s and recognised PAs play an important role in wildlife conservation and habitat

protection, informal PAs such as sacred groves and private wildlife sanctuaries are also an extremely

important part of Indian conservation efforts (Bhagwat et.al 2005). These areas along with semi-

protected areas such as Forests Reserves and Forests Ranges (IUCN Category VI) have a strong

influence on and support the ecosystems and biodiversity of the NPs and WSs that form the core of

the Indian conservation strategy. The Nilgiri Biosphere Reserve (figure 2.7) is a perfect example of

the combination of multiple PAs with varying protection statuses to try and provide comprehensive

levels of conservation. The NBR is spread over the states of Kamatika, Kerala and Tamil Nadu, the

forested areas of the reserve cover 5520km2, supporting all the major vegetation types of peninsular

India including; tropical ever-green and semi-evergreen forests, tropical moist deciduous and

tropical dry deciduous, with characteristic patches of tropical montane forests above 1800m and

extensive grassland on hill slopes (Kodandapani et.al 2004). The NBR, as a UNESCO biosphere

reserve, aims to promote sustainable development based on local community efforts and

conservation knowledge, reconciling conservation of biological and cultural diversity and economic

and social development through partnerships between people and nature (UNESCO 2013.b). While

there are numerous PAs in the NBR, the region is under immense human pressures. Grazing of

Figure 2-6: Panel a) site irreplaceabilities for achieving minimal reserve network targets in the WG, b) minimal reserve scenarios, c) minimal reserve network with existing protected areas highlighted and accounted for (Das et.al 2006).


14

livestock and wood cutting by humans adversely affect natural vegetation with continued

anthropogenic pressure likely to cause increased land degradation and LUCC in the near future

(Basharan et.al 2012). While the NBR is a good example of conservation efforts in the WG, it also

highlights the negative impacts of human activities in PAs and throughout the region.

3. Study Area

3.1. Western Ghats and the Nilgiri Biosphere Reserve

Figure 2-7: Protected Areas in and around the Nilgiri Biosphere Reserve (Conservation International 2013.b).

2.6 Research Questions and Hypotheses

Following comprehensive research on the global importance of LUCC and rates of LUCC throughout

the WG, this research project aims to evaluate changes to land-cover and land-use in Protected

Areas of the Western Ghats biodiversity hotspot.

Hypothesis Deforestation and increases in human land-uses will occur throughout PAs in the WGs, with high levels of LUCC.

Null Hypothesis There will be minimal LUCC within PAs of the WG with increasing rates of afforestation as human activities are reduced.


15

This study focuses on the PAs of the WG (figure 2.6, table 3.1). NPs and WSs in the WG preserve and

maintain a variety of environments, habitats, flora and fauna, many of which are endemic to one of

the most biodiverse hotspots on the planet. The PAs in this region represent some of the only

protected areas in the WG and as such are extremely important to conservation efforts. PAs

represent a majority of the vegetation cover in the WG, including; tropical ever and semi-green

forests, full and semi deciduous forests, scrub forests, shola and other grasslands and montane

forests at high altitudes, while a majority of land outside the parks contain agriculture, plantations

and degraded forests (Tewari 1995). Particular attention is paid to PAs in the NBR as it provides a

corridor of natural vegetation and forest habitats (figure 2.7) providing seasonal migration routes

and facilitating habitat supplementation (Bennett & Mulongoy 2006), which has come under

increasing pressure from human activities.

3.2. Silent Valley National Park

Silent Valley NP is located in the north of Kerala state, 76025’_-_76029’_E and 1104’_-_11013’_N, with

a core area of 89.52 km2 (KFWD 2013.a). Silent Valley forest cover mainly consists of tropical ever-

green forest and sub-tropical broad leaved hill forest while also containing patches of endemic

montane wet temperate forest. The park also contains hundreds of endemic plant species and is

home to one of the largest populations of the endangered loin-tailed Macaque (Manoharan 1999).

The park sits on the lower side of the Nilgiri plateau, which ranges from 500-2,000m in elevation,

annual rainfall averages around 5,000mm with temperatures ranging from 8-140C (min) – 23-290C

(Singh et.al 1984). The park is surrounded by ridges and steep escapements providing protection

against anthropogenic impacts, preserving one of the few tracts of virgin forest left in the WG and

one of the most complex and diverse vegetation communities on earth (Balakrishnan 1984). While

Silent Valley has mainly been bereft of human settlement due to its inaccessible location, human

interferences in the form of tree-felling, burning/fires and pre-dam1 construction have impacted

vegetation cover in the past (Singh et.al 1984).

1 A hydro-dam project was approved in 1978, before the area was given NP status (1984). This would have caused numerous detrimental impacts to the forested area including large amounts of flooding and forest cover loss (Singh et.al 1984). After wide-spread environmental protests the hydro-dam project was indefinitely postponed, however the growing demands for energy in India mean that dam construction in the park still remains a threat.

3. Study Area 3.1. Western Ghats and the Nilgiri Biosphere Reserve


16

a) b)

3.3. Mudumalai National Park and Wildlife Sanctuary

Mudumalai NP and WS extends over 321km2 within the Nilgiri plateau (11023’_–_11043’_N,_76022_

–_76045’_E) at an altitude of 850-1250m in the Indian state of Tamil Nadu (Sukumar et.al 1992).

Mudumalai is considered one of the most important PAs in the NBR, creating an extensive forest

cover with three other parks (Nagrahole and Bandipur NP and Wynaad WS) between the Western

and Eastern Ghats (Silori & Mishra 2001), critical for the migration of Indian Elephants and the

Bengal Tiger. The park has a distinct rainfall gradient from west (average 1800mm annually) to east

(800mm annually) giving rise to its distinctive vegetation cover; moist deciduous forests in the west

gradually give way to dry deciduous forests which ultimately become dry-thorn forests and wooded

grassland (Ganesan 1993). The importance of Mudumalai was officially realised in 1942 when it was

Figure 3-1: The states of Kerala (a) and Tamil Nadu (b) with the locations of wildlife protected areas including NPs and WS, as well as Silent Valley and Mudumalai, highlighted below (WII 2013, Federparchi 2013).


17

declared a wildlife sanctuary, in 1990 a large area of Mudumalai was given NP status and in 2007

was declared a tiger reserve (Tamil Nadu Forest Department 2013). While these all offer varying

levels of protection status, Mudumalai will be referred to as Mudumalai NP in this study, referring to

all areas of Mudumalai which are protected.

Mudumalai has been influenced by human activities and settlements, with tribes such as the

Kunumbas and Indas present in the area for several centuries (Hegde & Enters 2000). The human

population in Mudumalai NP have a high dependence on the land and forests for their livelihood

needs. Construction of a series of hydroelectric power stations around Masinagudi village has led to

a rapid increase in the parks population, a 143% increase between 1961 – 1991 (Silori & Mishra

2001) which is expected to continue on the near future.


18

Name Status Size (km2) IUCN Category Status Bannerghatta

NP

104.27 km2

II (1974)

Eravikulam

NP

97.0 km2

II (1978)

Kudremukh

NP

563.28 km2

II (1987)

Mudumalai

NP/WS/TR

217.76 km2

IV (1942) II (1990)

Silent Valley

NP

89.52 km2

II (1984)

Sharavathi Valley

WS

431.23 km2

IV (1974)

Indira Gandhi

NP/WS

841.49 km2

IV (1979) II (1989)

Bandipur

NP/TR

874.2 km2

II (1874)

Dandeli

WS

475.0km2

IV (1987)

Wayanad

WS

344.44 km2

IV (1973)

Parambikulam Sanctuary

WS/TR

285.0 km2

IV (1973)

Parambikulam (extension)

“ ”

120 km2

“ ”

Kalakad

WS/TR

223.58 km2

IV (1976)

Neyyar

WS

128.0 km2

IV (1958)

Peechi-Vazhani

WS

125.0 km2

IV (1958)

Bhadra

WS/TR

492.46 km2

IV (1974)

Nagarahole

NP/TR

643.39 km2

II (1974)

Mookambika

WS

247.0 km2

IV (1974)

Shettihalli

WS

395.6 km2

IV (1974)

Idukki

WS

70.0 km2

IV (1976)

Aralam

WS

55.0 km2

IV (1984)

Chinnar

WS

90.44 km2

IV (1984)

Anshi

NP/TR

250.0 km2

II (1987)

Cauvery

WS

526.96 km2

IV (1987)

Biligiri Ranga Swamy Temple

WS

539.52 km2

IV (1987)

Megamalai

WS

400.0 km2

IV (Final stages of a long-pending proposal to re-classify the park)

Someshwara

WS

88.4 km2

IV (1974)

Table 3-1: Protected Areas in the WG with available spatial and boundary data (IUCN & UNEP-WCMC (2013)


19

4. Data

Remote sensing data from numerous satellite platforms and instruments were evaluated to find the

most comprehensive and useful imaging data of Mudumalai and Silent Valley National Park. Remote

sensing images over India, specifically the WG, are extremely limited, especially before 1999. The

restricted amount of data sources for this region is most likely an influencing factor in regards to the

lack of research into one of the most biodiverse regions in World (Bava et.al 2007). Landsat data was

used to observe LUCC in and around Mudumalai NP and Silent Valley NP, while MODIS Vegetation

Continuous Field (VCF) (MOD44b) product data was used to analyse vegetation change across the

WG, incorporating numerous PAs.

4.1. Landsat Data

Landsat data is available in 185km x 170km scenes defined in a worldwide reference system (WRS-2)

of path and row co-ordinates (Hansen et.al 2008). The Landsat series provides one of the most

comprehensive remote sensing data sets providing relatively continuous global data since 1972. Five

Landsat datasets, including Landsat 1, 3 and 7 data from 1973 to 2012, covering 2 scenes (Path/Row

TM_1-3_155/52, TM-7 _144/53) were acquired (table 3.1). Multi-sensor approaches are most useful

when the variable of interest can only be measured infrequently and for limited areas (Lambin &

Linderman 2006) and so was the preferred approach when analysing LUCC of PAs in the WG.

The dry season across the WG occurs from January – June (IMD 2013) which is the most useful time

period for data on land-use classification (Xie et.al 2008) while also generally providing images with

low cloud cover(cloud-free optimum season) and less atmospheric interference (Jha et.al 2000).

Therefore Landsat data was acquired between January and May for each of the 5 scenes (table 3.1).

The Landsat acquisitions were chosen to provide a comprehensive temporal range while providing

accurate data for classification and analysis.


20

4.1.1. Pre-processing and Scene Selection

For all Landsat scenes (table 3.1) level 1b product data was downloaded from GLOVIS, Earth Explorer

(GLOVIS 2013.a), providing geo-referenced images of Mudumalai and Silent Valley NPs and the

surrounding areas. Landsat level 1b product provides systematic radiometric and geometric accuracy

which is derived from data collected by the sensor and spacecraft (GLOVIS 2013.b).

4.1.2. Reflectance Values from Digital Numbers

The Digital Numbers (DN) for each scene were converted to ground surface reflectance values (using

Eq. 1) to normalize the remotely sensed images. Absolute radiometric correction was applied to the

Landsat images using reasonable estimations of atmospheric optical depth, solar zenith angle and

satellite status (Chen et.al 2005) from specific data provided by Landsat with each data file.

).255(max).1(min.

...:

LMIN + QCALMIN)-(QCAL * ] QCALMIN)-)/(QCALMAXLMIN - [(LMAX = L 1) Eq.

=======

typicallyvaluepixelcalibratedquantizedimumtheQCALMAXtypicallyvaluepixelcalibratedquantizedtheQCALMINQCALMAXtoscalesradiancespectralLMAX

QCALMINtoscalesradiancespectralLMINnumberdigitalQCALradianceasvaluecelltheisLWhere

λ

λλ

λλλλ

Date

Platform Sensor Image

10.02.1973

Landsat 1 MSS LM11550521973005AAA02

23.05.1981 Landsat 3 MSS LM31550521981143AAA03

21.02.2003 Landsat 7

ETM+ LE71440522003052SGS00

18.01.2008

02.01.2008 (Mosaic Overlay)

Landsat 7

Landsat 7

ETM+

ETM+

LE71440522008018ASN00

LE71440522008002SGS00

01.03.2012

18.04.2012 (Mosaic Overlay)

Landsat 7

Landsat 7

ETM+

ETM+

LE71440522012061PFS00

LE71440522012109PFS00

Table 4.1: Landsat data used in this study

Eq. 1) Equation for conversion to ground surface reflectance values using specific data which accompanies each Landsat scene (Chen et.al 2005).


21

4.1.3. Data Concatenation and Layer Stacking

To aid further processing the individual spectral band files of each Landsat scene were concatenated

and written out into a multi-band data files. Landsat MSS bands 2-4 and Landsat ETM+ reflectance

bands 1-5 and 7-8 were used, while thermal band 6 (10.4-12.5 um) was excluded (figure 4.1). Each

scene could then be loaded as a ‘true colour composite’ as well as numerous ‘false colour

composites’ for detailed analysis and accurate land classification (figure 4.1).

4.1.4. Geo-registration of Landsat MSS Data

Landsat ETM+ data from 2003-2012 were all accurately geo-referenced and no geo-registration was

required. Landsat MSS data from 1973 and 1981 were geo-registered to the Landsat 2003 scene,

chosen as this was the only non-mosaic ETM+ dataset. In order to geometrically correct the satellite

data each Landsat MSS scene was geo-registered to the 2003 Landsat ETM+ image. Ground Control

Points (GCPs) were generated from common points (Jha et.al 2000) in the 2003 image and over 60

GCPs were created for both the MSS scenes with an RMS error of: 1973 – 0.462132, 1981 –

0.449332. Both MSS scenes were then warped (figure 4.2) using the resampling, scaling and

translation (RST) function with a nearest neighbour resampling method. Geo-registration of MSS

data provided geo-referenced Landsat scenes from 1973 and 1981 which could be accurately

compared with Landsat ETM+ data (figure 4.2).

Figure 4.1: Landsat 2003 multi-band file with a false colour composite (bands 4,3,2) image

displayed, WRS-2: path-144 row-53.


22

4.1.5. Atmospheric Correction

Atmospheric correction of remotely sensed data is necessary to remove any modifications by

scattering and absorption from aerosols and gases, as electromagnetic radiation signals travel

through the Earth’s atmosphere (Song et.al 2001). Corrections are essential to put temporal data on

the same radiometric scale (Song et.al 2001) in order to monitor terrestrial surfaces and land-use

change over time. Dark Object Subtraction (DOS) is one of the most widely used equations to adjust

for the effects of atmospheric scattering, with its limited capabilities, relative to more sophisticated

methods, offset by its wide applicability across numerous remotely sensed data sets (Campbell

1993). The ‘Starting Haze Value’ (SHV) (Eq. 2) was selected from a group of dark pixels which were

attained from an ROI created from pixels covering dark water surfaces. It is unlikely that most

images contain entire pixels that are truly black and a correction is applied which assumes a 1%

actual reflectance (Chavez 1996) (Eq. 2).

Figure 4.2: 1981 Landsat MSS geo-registered image and 2003 Landsat ETM+ image of Silent Valley National Park.

2003 Landsat ETM+ Silent Valley 1981 Landsat MSS Silent Valley


23

DOS converts the calculated LHaze value to at-sensor reflectance’s and subtracts it from the entire

image (Goslee 2011). This method was applied to every Landsat scene, for each spectral band, to

correct for any atmospheric impacts and allow for accurate comparisons of LUCC over time.

%1

22

%1

)

cos01.0))2.

LradSHVLb

d

ELaEq

haze

sun

−=

=π

θ

4.1.6. Landsat 7 ETM+ Scan-Line Correction Failure and Gap-Filling

On the 31st May 2003 the Landsat 7 ETM+ SLC failed causing line gaps in all Landsat scenes (Maxwell

et.al 2007). For the 2008 and 2012 datasets other Landsat scenes were acquired (table 3.1) to create

composite mosaics, providing consistent Landsat data that could be used to derive land-cover for

detailed regional assessment (Roy et.al 2010). The secondary scenes for gap-filling were selected as

close to the primary scene as possible to provide accurate and reflectively similar remote sensing

data, while also covering as much of the ‘absent’ pixels as possible. The Landsat USGS ‘Gap Phase

Statistic Calculator’ was used to calculate each scenes gap filling potential (USGS 2013.a) with over

95% mosaic image completion for both 2008 and 2012.

4.2. Classification

4.2.1. National Park Boundaries and Image Masking

The location and boundaries of Mudumalai and Silent Valley NP were acquired from ‘Protected

Planet’ (IUCN and UNEP-WCMC 2013), part of the ‘World Database on Protected Areas (WDPA) a

joint programme between the UN World Conservation Monitoring Centre (WCMC) and the

International Union for Conservation of Nature. The WDPA is the most complete dataset on the

world’s terrestrial PAs, providing freely accessible spatial and boundary data in the form of GIS

electronic maps (UNEP-WCMC 2013).

Eq. 2) a) 1% actual reflectance correction for SHV. b) the chosen DN value (SHV) used for DOS (Goslee 2011).


24

Landsat scenes were resized to cover the two NPs in separate images (figure 4.3a). A mask file was

then created from the park boundary data (figure 4.3b) and applied to each resized scene (figure

4.3c). A buffer zone of 5km was also created and applied (figure 4.3d) so that LUCC in the immediate

surrounding areas of the NPs could be analysed.

4.2.2. Regions of Interest and Transform Divergence

Regions of Interest (ROI) classifying land-cover types in the NPs were selected from analysis of

vegetation cover and land-use in the parks and surrounding areas of the WG (Menon and Bawa

1997, Prakasam 2010, Iman 2011, Giriraj et.al 2011, Gunawardene et.al 2007 and Singh et.al 1984)

several physiognomic classification schemes (Hansen et.al 2008, Rasool 1992, Jha et.al 2000) and

analysis of Landsat images. Transform Divergence (TD) was then applied, using all spectral bands, to

analyse the separability of the ROIs (Swain & King 1973). ROIs with low separability were then

merged and this process continued until four ROIs: Dense Forest, SWG, Agriculture

Figure 4.3a

Figure 4.3c Figure 4.3d

Figure 4.3b

Figure 4.3: a) Resized Landsat 2008 Scene of Mudumalai National Park (false colour composite bands 5,4,3). b) Mudumalai boundary vector layer from the WDPA (UNEP-WCMC 2013). c) Masked boundary of

Mudumalai NP. d) Masked boundary of Mudumalai NP with a 5km buffer zone.

(incorporating all human land-uses and degraded grassland cover) and Water remained with

acceptable separability statistics (Jensen 2005, Mutlu et.al 2008).


25

4.2.3. Classification and Post Classification

Mudumalai and Silent Valley NP were then classified (figure 5.1, 5.4) using all spectral bands for each

of the Landsat scenes between 1973 and 2012. MLC provides a probabilistic method for recognizing

similarities between individual measurements and pre-defined standards (classes), these classes are

characterised by a vector of means on measurement variables and a variance-covariance matrix

which define the interrelationships among variables characteristic of the class (Strahler 1980).The

MLC algorithm processes large amounts of information on class membership characteristics for each

pixel (Foody et.al 1992) providing thematic classification maps for purposes such as land-use/land-

cover determination (Strahler 1980) and is widely used when there are few spectral bands available,

as for example with Landsat MSS and TM data (Jia & Richards 1994). Majority_Minority (M_M) post-

classification analysis was applied to 2008 and 2012 classified images, using a 3x3 kernel majority

value on SWG and Dense Vegetation. M_M analysis was used to minimise the effects of stripping

(Qian et.al 2005) due to the mosaicking of two scenes.

4.2.4. Accuracy Assessment

An Accuracy assessment in the form of a confusion matrix was applied to the 2012 classification

images to assess class accuracy. Random stratified sample regions were selected for each class,

these points were imported into Google Earth and ground truth ROIs were created (figure 4.4) from

a 05/03/2012 Google Earth image file (Google Inc. 2012). Each ground truth polygon was then

imported into a GIS software, formatted into a geo-referenced shapefile which could be imported as

a ground truth ROI. A Confusion Matrix was then created for both Silent Valley and Mudumalai NPs,

from ground truth ROIs from 05/03/2012 compared with 01/03/2012 Landsat classified images.


26

4.3. MODIS Vegetation Continuous Field (VCF) Data

The MODIS VCF product is a global representation of the Earth’s surface as gradations of ground

cover; percentage tee cover, percentage non-tree cover and percentage bare, with each 250m pixel

shown as a sub-pixel mixture expressed as percentage ground cover (Carroll et.al 2011, Hansen et.al

2000, 2002). MODIS VCFs have the potential to characterise forest cover and vegetation change

(Hansen et.al 2008) over regional and spatially complex scales and depicting areas of heterogeneous

land-cover more accurately than traditional discrete classification schemes (GLCF 2013). MODIS VCF

datasets are currently available as annual products providing a yearly composite of ground cover

data between 2000-2010.

Figure 4.4:


27

4.3.1. Generating regional MODIS VCF 250m Land-Cover Maps

MODIS VCF geotiff data files were acquired from the Global Land-Cover Facility Database (DiMiceli

et.al 2011) providing geo-referenced ground cover data for Southern India (figure 4.5a) between

2000-1010. Each VCF file was resized and masked to Mudumalai and Silent Valley NPs boundaries

(figure 4.5b,c) as well as a 5km buffer boundary for each of the National Parks, from the WGCD

(IUCN and UNEP-WCMC 2013). Each ‘percentage tree cover’ map for the NPs were then thresholded

into ground cover classes. VCF pixel classification was based on several physiognomic classification

schemes (Hansen et.al 2008, Rasool 1992, Jha et.al 2000) while also allowing for seasonal vegetation

and tree cover changes and comparisons with Landsat classification images. For Mudumalai National

Park, VCF pixels with 19% tree cover and above were classified as Dense Forest , 9-18% tree cover

was classified as SWG and 0-8% tree cover as Agriculture (figure 4.5d). Water pixels were included in

classification (VCF value of 200), but were treated as a non-land surface so were excluded from LUCC

analysis. For Silent Valley National Park VCF pixels were categorized into the same two classes as the

Silent Valley Landsat classification images, with 40-100% tree cover classified as Dense Forest and 0-

40% tree cover classified as Degraded Forest.

Figure 4.5: a) MODIS VCF 2008 % tree cover image file of South India. b) MODIS VCF 2008 Silent Valley NP masked image. c) MODIS VCF 2008 Mudumalai NP masked image. d) MODIS VCF 2008 Mudumalai masked classification.

Figure 4.5a Figure 4.5c

Figure 4.5b Figure 4.5d


28

To analyse ground cover change and LUCC using VCF data, two separate VCF datasets were selected.

VCF data are annual composites and so are affected by annual variations in precipitation which can

influence vegetation cover (Krishnakumar et.al 2009, Pal & Al-Tabbaa 2011). Precipitation data from

the Indian Meteorological Department (IMD 2013) was used to analyse annual precipitation

variations which could be compared with the VCF files. The VCF data of Southern India during 2000

and 2008 were selected based on their similar annual precipitation values and VCF accuracy

compared with Landsat data. The 2000 and 2008 VCF classified images of Mudumalai and Silent

Valley NPs were then used to observe LUCC based on the MODIS VCF, over the 8 year period.

A Confusion Matrix for the 2008 VCF classifications of Mudumalai and Silent Valley NP provided

accuracy assessments of classifications. Ground Truth ROIs were selected using the same method for

Landsat Accuracy Assessments using the same Google Earth 05/03/2012 image file so that confusion

matrixes of Landsat and VCF data could be compared, and the differences in class accuracies

assessed.

4.3.2. VCF Land-Cover Maps Across the Western Ghats

To analyse LUCC in NPs and PAs throughout the Western Ghats, data from PAs in the region (table

3.1) with spatial boundary data was acquired from the WGCD (IUCN and UNEP-WCMC 2013). This

included 9 National Parks, 20 Wildlife Sanctuaries and 8 Tiger Reserves, with a total of 27 Protected

Areas, covering 2,349,952,603,326.93 meters2. The PAs were masked from the 2000 and 2008 VCF

files of Southern India. Pixels were thresholded into 4 ground cover classes, taken from the VCF

Mudumalai classifications and applied to all PAs. This provided LUCC and ground/vegetation cover

changes in PAs in the biodiversity hotspot region of the WG between 2000 and 2008 based on

MODIS VCF data.


29

Classification images of Mudumalai NP 1973 – 2012, show changes in land-cover between the

four selected classes and allow for analysis of LUCC. Dense Forest mainly covers the west of

Mudumalai NP and parts of the south, while SWG dominates the west of the park (figure 5.1).

Agriculture and Degraded Grassland can clearly be observed in three main areas; to the south-

west of the park, amongst the SWG in the north-east and surrounding the dam and rivers in the

middle of the park.

Legend

5. Results

5.1. Landsat 1973 – 2012Mudumalai National Park Maximum Liklehood Classified Data

Legend


30

Legend

Legend

Legend

Figure 5-1: Maximum Likelihood Classification Images of Mudumalai National Park 1973 – 2012.


31

Land-use classification of the 5km buffer zone around Mudumalai NP shows the extension of

Agriculture from the surrounding unprotected areas and into the national park (figure 5.2). There

are also sections of Agriculture in the south of the buffer zone and a large agricultural area in the

north of the buffer zone, close to the park boundary. Dense Forest dominates throughout the south

and south-west within the park and buffer zone. SWG is found in the north-east of the park and

buffer zone which contains sporadic areas of Agriculture close to the park boundary. SWG is also

found in intermittent patches in the south and south-west amongst large areas of Dense Forest.

Figure 5-2: Landsat 1973 and 2012 MLC images of Mudumalai National Park and 5km buffer zone with the park boundary highlighted in red.


32

-

10.00

20.00

30.00

40.00

50.00

60.00

70.00

1973 1981 2003 2008 2012

Prec

enta

ge T

otal

Lan

d Co

ver

Clas

s (%

)

Date of Landsat Data Collection

LUCC in Mudumalai National Park and 5km Buffer Zone Between 1973 - 2012 Based on Landsat MLC Data.

Dense Forest

Sparsely Wooded Grassland

Agriculture

Water

Figure 5-3: Chart showing class data of Mudumalai NP and 5km buffer zone for each Landsat classified image 1973 – 2012.

Table 5-1: Landsat class area data (m2) for Mudumalai NP.


33

5.2. Landsat 1973 – 2012 Silent Valley National Park Maximum Liklehood Classified Data

Figure 5-4: MLC images of Silent Valley National Park including Landsat data from 1973, 1981, 2003, 2008 and

2012.


34

Landsat 1973 Silent Valley National Park and 5km Buffer Zone Maximum Likelihood Classified Image

Landsat 2012 Silent Valley National Park and 5km Buffer Zone Maximum Likelihood Classified Image

Figure 5-5: MLC images of Silent Valley National Park and 5km buffer zone (with Silent Valley NP boundary in red) from Landsat data, 1973 and 2012.

Table 5-2: Landsat class area data (m2) for Silent Valley NP.


35

Classifications of Silent Valley NP provide evidence of degraded forest cover, focused in the centre

and to the south of the park with smaller and more intermitent patchs of degraded forest in the

north. (figure 5.4). Classification of Silent Valley’s buffer zone shows Degraded Forest cover focused

in the south with patchs in the north encroaching into the park. There is a decrease of Dense Forest

cover within the total classified area of -34,020,000.00 m2 between 1973 and 2012. Degarded forest

occurs more in the buffer zone (101,479,275.00 m2 / 33.16%) than within the park (22,109,850.00 m2

/ 199.98%) with a majority of degraded forest located near the park boundary. While cloud cover

does occur in the 1981 and 2003 Landsat images, these are seperately classified and statistcially

cover less than 2.1% in both images (figure 5.6).

-

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LUCC in Silent Valley National Park and 5km Buffer Zone Between 1973 - 2012 Based on Landsat MLC Data

Dense Forest

Degraded Forest

Cloud

Figure 5-6. Chart showing classification data of Landsat MLC images of Silent Valley National Park with a 5km buffer zone 1973 – 2012.


36

5.3. Accuracy Assesment – Confusion Matricies of Landsat MLC Images

Accuracy assesments of both Mudumalai and Silent Valley NP 2012 classified images produce very

high classification accuracies. Classification data accurately portrays land-cover in both NPs and can

be used to evalute LUCC from 1973 to 2012. For Mudumalai NP Agriculture classed pixels have the

lowest accuracy (Users Acc. 86.45%) with a 13.55% error of comission. The rest of the Users Acc. and

Producers Acc. are over 90% with classification of Dense Forest and Water classes being especially

high, with a total Overall Acc. of 93.44% (figure 5.7).

Silent Valley NP has a very high Overall Acc. of 97.29%. Degraded Forest class pixels have a Users

Acc. of 99.57% (figure 5.7). Such a high classification accuracy is most likely due to the use of only

two classes, the high spectral sperability of the two classes and accurate ROI selection.

Figure 5-7: Accuracy Assessment for 2012 Maximum Likelihood Classification images of Mudumalai and Silent Valley National Parks.


37

MODIS VCF 2000 Mudumalai National Park Classified Image

MODIS VCF 2008 Mudumalai National Park Classified Image

5.4 MODIS VCF Classified Data of Mudumalai National Park, 2000 - 2008

Figure 5-8: MODIS VCF 2000 and 2008 classified images of Mudumalai National Park based VCF %

tree cover data.

Table 5-3: MODIS VCF class area data (m2) for Mudumalai NP.


38

5.5. MODIS VCF Classified Data of Silent Valley National Park, 2000 – 2008.

Figure 5-9: MODIS VCF 2000 and 2008 classified images of Silent Valley National Park based on VCF % tree cover data.

Table 5-4: MODIS VCF class area data (m2) for Silent Valley NP.


39

MODIS VCF classifications show land-cover in Mudumalai and Silent Valley NP with LUCC between

2000-2008. Classification results use each pixels value of % tree cover to designate it to a pre-

classified class based on Landsat classification. Agriculture cover (0-8% tree cover) can be observed

mainly in the centre of the park and to the north-east mainly located in and amongst SWG. Dense

Forest (19-100%) and SWG 9-18% dominate the west and east areas of Mudumalai respectively with

the forest/grassland boundary located in the middle of the park, close to the only water pixel and

large areas of Agriculture. There is a decrease in Dense Forest between 2000-2008 of -47,061,858.70

m2 in the park, with increases of SWG and Agriculture at 41,879,295.84 m2 and 5,182,562.86 m2

respectively.

VCF classifications of Silent Valley highlight Degraded Forest cover in the south with smaller patches

throughout the north of the park. Between 2000-2008 there is an increase of Degraded Forest of

6,868,303.21 m2 to a total of 17,878,560.27 m2. There is even greater LUCC and increases in

Degraded Forest (48,707,280.02 m2) in the buffer zone of Silent valley NP with a total of

109,211,264.03 m2 in 2008.


40

5.6. LUCC In National Parks and Wildlife Sanctuaries Across the Western Ghats Between 2000 - 2008, Based on MODIS VCF Data

Figure 5-10: MODIS VCF 2000 and 2008 classified images of protected areas across the Western Ghats (table 3.1) based on VCF % tree cover data.

MODIS VCF 2000 Classified Image of National Parks and Wildlife Sanctuaries in the Western Ghats

MODIS VCF 2008 Classified Image of National Parks and Wildlife Sanctuaries in the Western Ghats


41

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LUCC In National Parks and Wildlife Sanctuaries Across the Western Ghats Between 2000 - 2008, Based on MODIS VCF Data

Dense Forest (19-100% Forest Cover)

Sparsely Wooded Grassland (9-18% Forest Cover)

Agriculture (0-8% Forest Cover)

Water

Table 5-5: MODIS VCF class area data (m2) for protected areas across the Western Ghats (table 3.1).

Figure 5-11: Chart of MODIS VCF classification data with percentage land-cover class values for NP’s and WS’s across the Western Ghats, 2000 – 2008.


42

LUCC in the selected NPs and WSs of the WG is contradictory to what would be expected based on

LUCC in Mudumalai and Silent Valley NP. For the total classified area in the WG between 2000 –

2008, LUCC of 768,351,070.60 m2 of Dense Forest, - 146,644,806.37 m2 of SWG and –

621,706,263.23 m2 of Agriculture occurred (table 5.5). This equates to an increase of Dense Forest

cover by 3.26% and a decrease of SWG and Agriculture/Degraded Grassland of 0.63% and 2.64%

respectively over the 8 year period (figure 5.11). Conversion of land-cover for SWG and Agriculture

to Dense Forest land-cover also occurred in the 5km buffer zone - 426,603,073.08 m2 compared with

341,747,997.53 m2 within the PAs.


43

5.7. LUCC in National Parks and Wildlife Sanctuaries Across the Nilgiri Biosphere Reserve Between 2000 - 2008, Based on MODIS VCF Data

Table 5-6: MODIS VCF class area (m2) data for protected areas in the Nilgiri Biosphere Reserve.

Figure 5-12: MODIS VCF 2000 and 2008 classified images of protected areas in the NBR, based on VCF % tree cover data.

MODIS VCF 2000 Classified Image of National Parks and Wildlife Sanctuaries in the Nilgiri Biosphere Reserve

MODIS VCF 2008 Classified Image of National Parks and Wildlife Sanctuaries in the Nilgiri Biosphere Reserve


44

Figure 5-13: Chart of MODIS VCF classification data with percentage land-cover class values for NP’s and WS’s of the NBR, 2000 – 2008.

LUCC for Nilgiri Biosphere Reserve, based on VCF classification data, is statistically similar (table 5.6)

to LUCC of the Western Ghats classified region (table 5.5). NBR LUCC for the total classified area

experienced an increase in Dense Forest cover (208,699,048.03 m2) at the expense SWG (-

95,647,117.57 m2) and Agriculture (113,051,930.45 m2). While including increases of SWG and

Agriculture land-cover from Mudumalai and Silent Valley NP, there is still a total decrease of these

land-cover types across PAs in both the NBR and WG classified areas. LUCC of all classes occur more

within the buffer zones of the PAs, highlighted by the increases of Dense Forest of 147,967,360.54

compared with 60,731,687.49 within the park boundaries between 2000-2008.

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Dense Forest (19-100% Forest Cover)

Sparsely Wooded Grassland (9-18% Forest Cover)

Agriculture (0-8% Forest Cover)

Water


45

5.8. Accuracy Assesment – Confusion Matricies of MODIS VCF Classified Images

While Users Accuracy is high for most classes in both parks (SWG in Mudumalai is the anomaly with 58.33% accuracy), Producers Accuracies in the parks are varied with Agriculture and SWG at 70% accuracy in Mudumalai and Degraded Forest at 33.33% accuracy in Silent Valley. Total classification accuracies however are high for both parks, with accuracies of 76.47% for Mudumalai NP and 84.62% for Silent Valley NP.

Figure 5.14: Accuracy Assessment for 2008 MODIS VCF classified images of Mudumalai and Silent Valley National Parks.


46

6. Discussion

6.1. LUCC in Mudumalai National Park

Classifications of Mudumalai NP (figure 5.1) accurately depict the distinctive vegetation cover from

dense (moist deciduous) forest to SWG (Gansen 1993) which is created by the strong rainfall

gradient from west to east. Land-use created by the small human population in the park has also

been clearly depicted (Agriculture Class) especially around the hydro-electric power stations near

Masinagudi village. One of the most noticeable LUCCs in Mudumalai NP is the expansion of

agriculture and human land-use around the river networks, small settlements and hydro-electric

power stations near the centre of the park (figure 5.1) with a total net increase of 10,937,025 m2

since 1973 (table 5.1). The increases in population suggested by Silori and Misha (2001) with the

combined pressures of tourism and agriculture in the 1970s and 80s are still currently causing LUCC

to a more human dominated landscape within the park. LUCC to agriculture in the north-east of the

park is more sporadic and spatially varied, symptomatic of land degradation caused by the impacts

of livestock grazing, fire and collection of NWFPs. Agriculture and Degraded-Grassland can also be

observed encroaching into the Dense Forest vegetation in the south-west of the park, with

significant LUCC in 2008 (figure 5.1). This trend of encroachment is not well represented in the 2012

classified image, partly affected by line stripping in the classification and by pixel mixing due to

vegetation diversity, interspersion and seasonal changes to forest tree cover shadowing the small

tracts of agricultural land (Friedl & Brodley 1997), underestimating agricultural land-cover in this

area. Classification accuracy for Mudumalai NP was high (total acc. 93.44%) however the Agriculture

class had a 13.55% error of commission, mainly from SWG pixels potentially leading to a slight over-

estimation of human land-use change. Despite the possibility of over-estimation there is still a clear

and continuous trend of agricultural expansion in Mudumalai NP providing evidence of growing

human activities and land uses within the park.

Another noticeable trend in Mudumalai NP is the continuous and steady state at which LUCC occurs.

Post-classification analysis shows a relatively continuous decrease in Dense Forest with increases in

Agriculture and SWG for each of the Landsat classified images (table 5.1). There are some

irregularities in LUCC between 1973 and 1981 such as an increase in Dense Forest of 17,668,350 m2

and a decrease in Agriculture of 2,090,250 m2, most likely due to the coarse and imprecise data from

the Landsat MSS Sensor. This is generally an accepted limitation when assessing LUCC from remote

sensing data as early as 1973. The expansion of SWG since 1981 (table 5.1) is mainly at the expense


47

of Dense Forest vegetation cover to the west of the park (figure 5.1) with a total increase of

7,191,450 m2. LUCC trends in Mudumalai NP focus around the increase in Agriculture and human

land-uses mainly at the expense of SWG, which itself encroaches into the Dense Forests causing

decreases in Dense Forest cover. These trends are relatively constant and continuous in nature

providing strong evidence that such LUCC will continue in the future unless intervening action is

taken.

6.2. LUCC in Silent Valley National Park

Landsat classification images of Silent Valley NP highlight the expansion of degraded forest cover,

focused in the south and to the north/north-east, encroaching along park boundaries (figure 5.4).

Between 1973 – 2012 there was an overall increase of degraded forest cover of 6,361,200 m2. This

equates to a total of 5.75% in LUCC throughout the NP. Similar to the LUCC in Mudumalai NP, Silent

Valley LUCC is relatively consistent with constant increases in degraded forest for each Landsat

classified image. This provides evidence that forest disturbance and degradation has not occurred

due to any particular event or large change within the park but is a continuous process degrading

the forest vegetation gradually over time.

There is a distinctive isolated patch of degraded forest cover in the middle of the park, unlike any

other section of degraded forest as it does not clearly change in size or extent over time. It is

possibly a result of pre-dam construction which has been indefinitely postponed since 1984 (Singh

et.al 1984) or it may also be the result of increasing tourism in Silent Valley (KFWD 2013.b), however

the lack of spatial change suggests the former. Degraded forest encroaching in the north/north-east

of the park occurs sporadically, providing evidence of impermanent and un-systematic human land-

use such as livestock grazing, fires and collection of NTFP (KFWD 2013.b). To the south of the park

there are large sections of degraded forest cover, with LUCC and forest disturbance appearing more

systematic with large areas of change, indicative of plantations, cultivation, tourism and more

permanent land uses. While there are only two classes for the classifications of Silent Valley

providing less statistical data than Mudumalai, there is still clear evidence of LUCC and forest

disturbance within the park. The governments of India and the state of Kerala should take note on

not only the direct but in-direct impacts of hydro-electric power stations on LUCC and land

degradation in PAs, such as Mudumalai NP. Pre-dam construction has already caused forest

degradation in Silent Valley and a hydro-electric dam/power station in the park will only increase the

rate of LUCC and forest disturbance, which are already at significant levels.


48

6.3. LUCC in the Buffer Zones of Mudumalai and Silent Valley National Parks

Land-cover and LUCC in the buffer zones around Mudumalai and Silent Valley are significantly

different than within the park boundaries. In 1973 degraded forest covered 24.12% of Silent Valley’s

buffer zone while Agriculture accounted for 12.00% of Mudumalai’s buffer zone, much higher levels

of human land-use than within the parks. In Silent Valley 27,658,800 m2 (9.04%) of land has changed

from Dense to Degraded Forest in the buffer area compared with 5.75% within the park boundary

since 1973. In 2012 29.66% of the total classified area around Silent Valley NP was degraded forest

(figure 5.6). Classification image stripping in 2012 (caused by Landsat’s SLC failure) (figure 5.5) causes

an under-estimation of degraded forest in the south, not fully highlighting the comprehensive areas

of degraded forest cover and slightly limiting the accuracy of LUCC detection in the park.

The most evident LUCC in Mudumalai NP buffer zone between 1973 and 2012 is the increase of SWG

(41,898,600 m2) mainly at the expense of Dense Forest (table 5.1, figure 5.3). Agriculture land-use

expands dramatically through the north-east section of the buffer zone, with smaller areas of

expanding Agriculture and SWG to the south and south-west of the buffer zone, encroaching into

Dense Forested areas. In 2012 14.39% of the buffer zone was classed as agriculture and while there

was only an increase of 2.3% over the 39 year period, agricultural land-cover still represents a large

proportion of the buffer zone. The increase of Agriculture and SWG have come at the expense of

Dense Forest which has decreased by 61,100,325 m2 in the buffer zone since 1973, covering 48.94%

of the total classified area in 2012 (figure 5.3).

Analysis of land-cover and LUCC within the PAs and in the surrounding buffer zones show that the

NPs are limiting development activities and human land-use expansion with the park boundaries.

Mudumalai is experiencing more LUCC than Silent Valley, which is to be expected due to parts of the

PA having WS protection status, human settlements and hydro-electric power stations. LUCC in

Mudumalai NP shows that the protection status of neighbouring Bandipur NP (to the north-west of

Mudumalai) is providing conservation support for natural vegetation cover and habitats in the NBR,

while the bordering PAs of Talaimalai FR, Nilgiri North FD and Wayanard WS are experiencing large

amounts of LUCC and deforestation rates along the borders of Mudumalai NP impacting on the

natural vegetation cover (figure 5.2). While there is less LUCC within the PAs than their

corresponding buffer zones, highlighting the conservation impacts PAs have, rates of LUCC with

increases in Agriculture and Degraded Forests along with decreases in Dense Forest cover are still

high. In the total classified areas, Silent Valley has experienced an increase of 34,020,000 m2 (8.17%)

in degraded forest while Mudumalai experienced an increase of 41,898,600 m2 (4.18%) and


49

25,421,400 m2 (2.53%) in SWG and Agriculture respectively. An improved conservation effort,

management of human activity and land-use and continued monitoring of these NPs is essential to

maintain and improve the protection that the parks offer to the natural vegetation, habitats and

biodiversity within their boundaries.

6.4. Analysis of MODIS VCF Classified Data; Mudumalai and Silent Valley National Parks

Classifications using VCF data have accurately depicted land-cover and land-use in Mudumalai and

Silent Valley NP (figure 5.8, 5.9). By using Landsat high resolution classifications to aid and interpret

MODIS VCF data, land-cover has been accurately demarcated and provides similar land-cover spatial

characteristics to Landsat classifications (figure 5.1, 5.4). In Mudumalai NP the VCF 2008

classification shows the distinctive vegetation gradient of Dense Forest to Forest boundaries and

eventually SWG from west to east, and accurately highlights Agriculture and human land-use

(Landsat 2008 Mudumalai NP Agriculture = 36,033,975 m2, VCF 2008 Mudumalai NP Agriculture =

34,445,720 m2). Silent Valley NP VCF classification data (figure 5.9) also has similar spatial depictions

of land-use compared with Landsat classifications with Degraded Forest cover predominantly found

to the south, with patches in the north and a distinctive area in the centre of the park. There are also

significant statistical similarities between the two different classification datasets; Degraded Forest –

21,886,650 m2 (Landsat) and 17,878,560.27 m2 (VCF), Dense Forest cover of 88,731,675 m2 (Landsat)

and 93,482,325.38 m2 (VCF). LUCC detection of Mudumalai and Silent Valley using VCF data

between 2000 – 2008 also show trends that strongly correlate with Landsat classifications. The most

distinctive trends being decreases in Dense Forest (-47,061,858.70 m2) and increases in SWG

(41,879,293.84 m2) and Agriculture (5,182,562.86 m2) in Mudumalai and an increase of Degraded

Forest cover (6,868,303.21 m2) in Silent Valley.

Accuracy assessments in the form of a Confusion Matrix for Mudumalai and Silent Valley (figure

5.14) show a total accuracy for classification of land-cover at 76.47% and 84.62% respectively. While

these accuracies are significantly lower than Landsat classifications and can be seen as a clear

limitation, accuracies above 75% when analysing small areas (NPs) using global land-cover products,

are relatively high. In fact, accuracies of 76.47% and 84.62% show the ability of VCF GLCD to analyse

land-cover and LUCC. The accuracy in land-cover classification of Mudumalai and Silent Valley NPs

and the surrounding areas using VCF data, shows they have the ability to detect LUCC in NPs and

therefore can be used to accurately observe LUCC in numerous PAs over a much larger spatial scale,

such as the whole of the WG.


50

6.5. LUCC in PAs of the Western Ghats and Nilgiri Biosphere Reserve using MODIS VCF Data.

LUCC between 2000-2008 in and around 9 NPs and 20 WSs across the WG provide evidence of

afforestation with increases of Dense Forest cover throughout the region. Within the PAs there is a

total increase in Dense Forest cover of 341,747,997.53 m2 at the expense of both SWG and

Agriculture, with even greater increases (426,603,073.08 m2) in the buffer zone creating a total

increase in Dense Forest of 768,351,070.60 m2. MODIS VCF data on LUCC when focused on the NBR

(figure 5.12) also shows an increase of Dense Forest 208,699,048.03 m2, with deceases in SWG

-95,647,117.57 m2 and Agriculture -113,051,930.45 m2 (table 5.6). Both Mudumalai and Silent Valley

NP show trends of deforestation with increases of SWG and Agriculture when using both Landsat

(1973-2012) and MODIS VCF (2000-2008) data. This supports research (Gunawardene et.al 2007, Jha

et.al 2000 and Chandrashekara & Ramakrishnan 1994) which provides evidence of deforestation and

increasing human land-uses in the WG. This is in stark contrast however to LUCC trends for the total

area of PAs in the region. LUCC detection from MODIS VCF classifications (figure 5.10, 5.12) provides

contradicting evidence of trends in afforestation and decreases in SWG and Agriculture for the total

land-cover in and around the PAs of the WG between 2000-2008.

While the majority of research on LUCC in India provides evidence of deforestation, most of this

research occurs before 2000, with Jha et.al (2000) providing one of the most comprehensive

datasets, still only for LUCC until 1995. More recent research (Bhat et.al 2001, Rudel et.al 2005)

provide evidence of LUCC in the form of afforestation in the past decade. VCF data on LUCC in the

WG and NBR supports recent research that suggests increasing demands for forest products

associated with income, population growth and previous rapid agricultural expansion has led to

increases in large scale plantations and rehabilitation programmes resulting in forest growth (Foster

& Rosenzwig 2003). Rudel et.al (2005) suggests that plantation and rehabilitation programmes are

mainly implemented by land-owners and farmers planting trees on their agricultural land. This

corroborates with LUCC data for both the WG and NBR where a majority of increases in Dense

Forest come from decreases in Agriculture and human land-uses (WG -621,706,264,23 m2, NBR -

113,051,930.00 m2) rather than SWG land-cover. Providing evidence that afforestation is coming

from changes in agricultural land and therefore most likely from plantations and re-forestation

programmes. Some errors in classification may occur when using VCF data as they are annual

composites and therefore affected by seasonal/annual variations in tree cover. There may also be

some inaccuracies caused from the designation of classes, for example anything above 19% tree


51

cover was classified as Dense Forest. While these may provide limitations to the LUCC data from VCF

classifications and impact on the high levels of afforestation, trends of increasing Dense Forest cover

with decreases in SWG and Agriculture are clearly evident and appear too large to be the result of

any limitations from the use of MODIS VCF data.

While LUCC in the form of afforestation means that there are decreases in Agriculture land-cover in

and around many of the PAs in the WG, total Agriculture land-cover is relatively high for PAs with

IUCN II, IV statuses. For PAs in the NBR alone there is a total of 1,625,207,466.26 m2 (20.38%) of

Agriculture land-cover in 2008. Agriculture covers 3,177,198,333.67 m2 (13.50%) in and around the

PAs of the WG, despite recent levels of afforestation this is still a large proportion of total land-

cover. While afforested areas in the WG (figure 5.10) are classified as Dense Forest, it is very

important that these areas are not mistaken for natural vegetation cover. A majority of these

afforested areas are most likely plantations and secondary forests (Bhat et.al 2001) and therefore

under human management and created for the production of forested goods rather than

conservation. LUCC in the form of afforestation between 2000-2008 (figure 2.10) is positive for

conservation efforts in the WG and PAs throughout the region. However such trends are driven by

the increasing demand for forest products due to income and population growth which are

continuing to increase the pressure on natural vegetation cover and forests in the WG. Current

trends of afforestation are part of a much larger economic trend of increasing commercial demands

for resources throughout India which is resulting in large increases of human land-use. Plantations

and secondary forests themselves can be seen as a negative impact of human land-use on

biodiversity, natural/virgin vegetation cover and the natural habitats of numerous endemic flora and

fauna in the WGs.


52

7. Project Limitations and Future Research

7.1. Project limitations

While post-classification analysis of multi-temporal data provides detailed and relatively accurate

statistics on LUCC and is one of the most popular methods of LUCC detection, there are still a

number of limitations impacting on the accuracy of results. Accuracy assessments for both

Mudumalai and Silent Valley 2012 Landsat classifications have a total classification accuracy of

93.44% and 97.29% respectively. While these are high for classification accuracies, miss-classified

pixels still represent 6.56% and 2.71% for Landsat classifications. The impacts of image stripping

resulting from methods to remove the impacts of Landsat 7 SLC failure after 2003, and the relatively

poor quality of MSS Sensor data, provide inherent limitations. These are however expected when

using Landsat remote sensing data and methods to mitigate these negative impacts on classifications

have been implemented. The use of only four land-cover classes for Mudumalai and two for Silent

Valley also limits analysis of LUCC, for example there is no distinction between human land-uses. The

use of a select number of classes was chosen however, so that classification accuracies would be

high and that the most apparent trends of LUCC would be clearly highlighted for further analysis and

interpretation.

The use of MODIS VCF data provides a number of limitations which are inherent when using GLCD,

most of which have been mentioned in the discussion of the MODIS VCF classifications. As a product

of a GLCD with a 250m spatial resolution, VCF classifications are less accurate than Landsat

classifications of Mudumalai and Silent Valley NP (figure 5.7, 5.14). Confusion Matrixes on MODIS

VCF classifications show the limitations in class accuracy, specifically of Degraded Forest in Silent

Valley NP.MODIS VCF accuracy assessments were also affected by the low number ground truth

pixels which was a result of MODIS VCFs low spatial resolution (250m). MODIS VCF classes were

based on the select few classes from Landsat data of the two parks. While this allows for analysis of

the most apparent and significant LUCC trends, natural vegetation cover present in other NPs in the

WG, such as Wetlands and Shola Grasslands will have been wrongly depicted as one of the four

classes from Mudumalai NP.

7.2. Future Research

A number of changes and improvements to future research could provide increased and more

detailed evidence of LUCC in PAs across the WGs. An increased buffer zone of 10 or 20km would

provide a more comprehensive overview of LUCC in the surrounding areas of the PAs. Further


53

analysis of vegetation cover types in PAs other than Mudumalai and Silent Valley would create a

more inclusive set of land-cover classes for MODIS VCF data. Improved accuracies of VCF

classifications will also occur in the near future with the incorporation of areal proportions of life

form, leaf type and leaf longevity layers in VCF data in future product releases. While a majority of

PAs were accounted for a number of NPs and WSs were omitted as no spatial and boundary data

could be obtained. While this is not a direct limitation on the accuracy or validity of the project’s

results, incorporation of all PAs would provide a more comprehensive dataset and analysis of LUCC

in PAs of the WG.

The most unexpected trend in LUCC was that of the large levels of afforestation for the total

classified area of the 27 PAs. Further analysis into LUCC in specific parks where there are high levels

of afforestation would provide important research on a contentious topic of LUCC and deforestation

rates in the WG biodiversity hotspot. The spatial patterns of afforestation in PAs and their buffer

zones, the types of land-cover or land-use which are being afforested and the vegetation cover/tree

types which cover the afforested areas pose important questions for future research on LUCC in the

biodiversity hotspot.

8. Conclusion

Landsat and MODIS VCF classification data provides evidence of deforestation, with LUCC to SWG

and areas of Agriculture and human land-uses in Mudumalai NP and Degraded Forest cover in Silent

Valley NP. Increases of Agriculture particularly have led to the fragmentation of natural vegetation

cover which is negatively affecting the wildlife corridor of the NBR.

This project presents a methodology to map land-cover and detect LUCC in PAs of the WG region

using Global MODIS VCF data. The method demonstrates the ability of VCF data to provide accurate

detection of LUCC in selected areas from a much larger region, PAs in the WG. Post-classification

analysis of VCF data highlights the current trend of afforestation in a majority of the PAs. While

MODIS VCF classification data is currently less accurate than high-resolution Landsat classifications

and provides a simplified version of land-cover in the PAs, it allows for a relatively easy, quick, cost-

effective and accurate analysis of LUCC over vastly larger areas than a single Landsat scene could

provide. With new advancements and layers in VCF data providing more accurate and detailed land-

cover depictions, LUCC detection from MODIS VCF data will continue to develop, providing valuable

information and improving our understanding of land-cover changes to the Earth’s surface.


i

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