landuse/land cover mapping using remote sensing and...

Chapter 7 Landuse/Land Cover Mapping

362 A WATERSHED APPROACH FOR SUSTAINABLE ECOSYSTEM MANAGEMENT OF MEENACHIL RIVER BASIN WITH EMPHASIS ON REMOTE SENSING AND GIS

LANDUSE/LAND COVER MAPPING USING REMOTE

SENSING AND GIS Objectives

1. To map and analyze landuse and land cover in the Meenachil river ecosystem.

2. To monitor and assess their temporal (1967– 2013) and spatial changes by

analyzing the satellite and field data.

7.1 Introduction and review of literature Streams and rivers are among the ecosystems most affected by human activities

(Dynesius and Nilsson, 1994; Master et al., 1997; Naiman and Turner, 2000). The species

composition (Richter et al., 1997; Jansson et al., 2000a), food web structure (Wootton et

al., 1996), nutrient cycling (Johnes, 1996; Meyer et al., 1999; Vörösmarty et al., 2000a),

and utility (Postel, 2000; Vörösmarty et al., 2000b) of thousands of kilometers of streams

and rivers worldwide have been greatly altered from their natural states. One of the main

causes of these alterations is landuse change. Landuse is changing rapidly around the

world (Mc-Closkey and Spalding, 1989; Vitousek et al., 1997; Turner et al., 1998); by

1994 about 75% of the habitable part of the planet was disturbed by human activity

(Hannah et al., 1994). Landuse change affects stream ecosystems by altering the timing,

amount, and kind of inputs of water, light, organic matter, and other materials to the

channel, which can have profound consequences for all aspects of the stream ecosystem.

The precise relationships between landuse conversion and ecological responses

are difficult to establish because: (1) the types of landuse, rates of conversion, and spatial

distribution of landuse vary considerably among watersheds and regions and across

political boundaries, (2) changes in landuse can drive channel morphology and hydrology

into a state of flux that may take many decades to stabilize (Fitzpatrick and Knox, 2000),

Cha

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(3) ecological responses may lag behind physical habitat modifications (Harding et al.,

1998) and (4) management actions have been introduced to mediate the effects of

development on streams, yet we know little about their effectiveness. Thus,

understanding and predicting the effects of landuse change on stream and river

ecosystems are difficult scientific problems and major challenges for contemporary

ecology.

All available approaches to studying this problem have significant weaknesses.

The ideal approach— replicated long-term experiments of entire watersheds—is usually

prohibitively costly, logistically impractical, and requires decades to deliver an answer.

Experimentation at smaller scales of space and time can still be costly and logistically

difficult. The results of such studies may be difficult to generalize to other sites. Further,

unless the experimental study is long-term, its findings may be dominated by transient

responses of the system that do not resemble its long-term responses (Tilman, 1989).

Uncontrolled observations on streams whose watersheds simply differ in land cover

suffer from problems of inferring cause from correlational patterns that are confounded

by numerous uncontrolled and cross-correlated variables, large cross-site variability, and

time lags (Pickett, 1989). Finally, mechanistic models linking land cover to ecological

responses of stream ecosystems can be difficult to construct (Nilsson et al., 2002) and

may be burdened with large and unknowable errors. Thus, no single scientific approach is

adequate for understanding the effects of changing landuse on stream ecosystems.

Despite the difficulties with each of these approaches, each clearly can contribute

significant (and sometimes unique) information about how land-cover change affects

stream and river ecosystems. Ultimately, satisfactory understanding of the ecological

effects of land-cover change in streams and rivers, like many complex ecological

problems, probably will require a creative combination of these approaches (Carpenter,

1998).

One approach that can help us understand how changes in land cover affect

stream ecosystems is empirical modeling (Omernik, 1976; Peters, 1986; Cole et al., 1991;

Shipley, 2000). Empirical models can be constructed using either data collected expressly



for the model or suitable data collected for other purposes and appropriated for use in the

model (“third-party models”). The potential of this latter class of models is growing

rapidly as environmental monitoring and research programs grow and as large data sets

are posted over the Internet.

An important issue with empirical (and other) models is spatial scaling of

variables. Stream ecosystems are affected by processes occurring at different spatial

scales, from local shading caused by the canopy directly over a place on the streambed to

regional loading of materials from distant parts of the water- or airshed. As tools for the

analysis of Geographic Information System (GIS) become more widespread and

sophisticated, it will increasingly become possible to choose the scale at which to model

these effects to optimize predictive power and understanding.

Two particular scaling issues commonly confront scientists interested in the

effects of land cover on stream ecosystems. The first (which we call “spatial extent”) is

the effect of watershed size on the predictive power of empirical models. Large

watersheds usually contain many different land-cover patches. The response of the stream

is determined jointly by many landscape elements, so nonspatially explicit variables, such

as the percentage of the watershed in a given land-cover class, may be adequate

predictors of land-cover effects on streams and rivers. In very small watersheds, though,

the number of land-cover elements usually is small, so the idiosyncrasies of the spatial

arrangement or management of such individual elements may have strong effects on

stream ecosystems. Thus, we expect the predictive power of models based on

nonspatially explicit landscape variables to decline in watersheds below some threshold

size, at which point the characteristics of individual landscape elements become

important.

The second issue has to do with the distribution of land cover within the

watershed. Presumably, landscape elements near the stream or river have more influence

on its ecosystem than elements in a distant part of the watershed. Although land cover

often is assessed for the entire watershed, it is equally possible to assess land cover in the

riparian corridor or local area around a sampling point to try to account for this increased



influence of near stream landscape elements. Such alternative assessments of land cover

(which we refer to as “spatial perspective”) may have different predictive power for

different ecological response variables in the stream or river.

In the last three decades, the technologies and methods of remote sensing have

evolved dramatically to include a suite of sensors operating at a wide range of imaging

scales with potential interest and importance to planners and land managers. Coupled

with the ready availability of historical remote sensing data, the reduction in data cost and

increased resolution from satellite platforms, remote sensing technology appears poised

to make an even greater impact on planning agencies and land management initiatives

involved in monitoring land-cover and landuse change at a variety of spatial scales.

Current remote sensing technology offers collection and analysis of data from ground-

based, atmospheric, and Earth-orbiting platforms, with linkages to GPS data, GIS data

layers and functions, and emerging modeling capabilities (Franklin, 2001). This has made

remote sensing a valuable source of land-cover and landuse information. As the demand

for increased amounts and quality of information rises, and technology continues to

improve, remote sensing will become increasingly critical in the future. Therefore, the

focus of this chapter is on the issues and challenges associated with monitoring land-

cover and landuse change.

Planning and land management agencies have numerous and varied

responsibilities and tasks (Jensen and Cowen, 1999). Further, their ability to complete

these tasks is hampered by the paucity of comprehensive information on the types and

rates of land-cover and landuse change, and even less systematic evidence on the causes,

distributions, rates, and consequences of those changes (Loveland et al., 2002). For

example, at the rural–urban fringe, large tracts of undeveloped rural land are rapidly

converted to urban landuse. This landuse dynamic makes it difficult for planners to obtain

or maintain up-to-date land-cover and landuse information, where typical updating

processes are on an interval scale of 5 years (Chen et al., 2001). Although the full

potential of remote sensing technology for change detection applications has yet to be

completely realized, planning agencies at local, regional and international levels now



recognize the need for remote sensing information to help formulate policy and provide

insight into future change patterns and trends (Jensen and Cowen, 1999).

Remote sensing information, in concert with available enabling technologies such

as GPS and GIS, can form the information base upon which sound planning decisions can

be made, while remaining cost-effective (Franklin et al., 2000). Clearly, however, the

fast-paced developmental nature of remote sensing technology often overlooks the needs

of end-users as it ‘…continues to outpace the accumulation of experience and

understanding’ (Franklin, 2001). As a result, effective real-world operational examples of

land-cover and landuse change remain relatively rare (Loveland et al., 2002; Rogan et al.,

2003).

In the near future, the field of remote sensing will change dramatically with the

projected increase in number of satellites of all types (Glackin, 1998). This will further

compound the problems described above. In order to help create a better understanding of

the rapid advancements in remote sensing technology that has occurred over the last three

decades, we review the current state of remote sensing technology (i.e. sensors, data,

analysis methods and applications) for monitoring land cover and landuse. Specifically,

we provide a brief history of the advances in remote sensing technology, and a review of

the major technical considerations of land-cover and landuse monitoring using remote

sensing data.

Early applications of remote sensing technology were largely experimental, but

soon led to an expanding field of land-cover and landuse classification to establish

baseline conditions for natural and urban/suburban areas (Lunetta, 1998). These efforts

were aided by the hierarchical land-cover classification scheme developed by Anderson

et al. (1976), which established guidelines for remote sensing mapping efforts, and its

influence persists today (Franklin et al., 2003). With improved understanding of land-

cover processes and improved means to observe them, researchers began investigating

both the patterns and processes of land-cover and landuse change in a variety of

environments, including; change in vegetation canopy and/or shrub cover (Singh, 1989;

Levien et al., 1999); change in urban/suburban cover (Chan et al., 2001); wetland



monitoring (Jensen et al., 1995; Phinn and Stanford, 2001); and crop mapping and

monitoring (Fang, 1998; McNairn et al., 2002). Recent applications have moved into the

realm of land cover and landuse modelling for ecosystem sustainability assessments in

natural and agricultural areas (Moulin et al., 1998; Vine and Puech, 1999), and projected

growth assessment of urban/suburban areas (Clarke and Gaydos, 1998).

7.2 Materials and methods Selected subwatersheds of Meenachil River Basin of Kerala, India were considered for

this study. Survey of India (SOI) topographic maps (58 C/6, 58 C/9, 58 C/10, 58 C/11,

58 C/13, and 58 C/14) on a scale of 1:50,000, IRS-IB-LISS II FCC 234 satellite imagery

acquired on March 5, 1996 (P25/R62) and IRS-P6-LISS-III data acquired on February 9,

2004 (P100/R67) and IRS-LISS 2007 image covering the Kottayam area with a

resolution of 22.5 meter were used for landuse classification.

Base maps including road, railway, settlement, village location and watershed

boundary extracted from the topographic sheets and converted into GIS database and

further the modifications in the LULC map updated by cross correlating with Remote

Sensing Imageries. The satellite digital data was rectified using Survey of India (SOI)

topographic maps; a reconnaissance survey was carried out to collect the ground

information. The GIS database generated from the topographic sheets was further

updated with the latest changes in the watershed. The image elements were correlated

with ground truthing and the interpretation key was developed. The tonal variation

representing the different classes was marked on the hard copy image. The different

landuse classes were identified as per classification system developed by NRSA with

modifications (Table 4.1).



Table 7.1: Landuse/land cover classification system

Sl No. Level I Level II

1 Built-up land Built-up land

2 Agricultural land

Crop land

Fallow land

Plantation

3 Forest

Evergreen/semi-evergreen forest

Deciduous forest

Degraded or scrub land

Forest plantation

Mangrove

4 Wastelands

Salt affected land

Water logged area

Marshy / swampy land

Gullied / ravenous land

Land with or without scrub

Sandy area, coastal or desertic

Barren rocky / stony waste / sheet rock area

5 Water bodies River/stream/lake/reservoir/tank/canal

6 Others

Shifting cultivation

Grassland / grazing land

Snow covered / glacial areas

Source: NRSA, 1999

Landuse categories such as agriculture, riverbed, water, urban, fallow, wasteland, etc.

have been identified and mapped from the SOI topographic sheets. The landuse of 1967

was mapped, classified and calculated accurately from the toposheets and it was

compared with those prepared from the satellite imageries (IRS 1B & P6 (LISS II & III)).

IRS-P6-LISS-III data was used as the source for the landuse/land cover mapping. The



interpretation key formulated during fieldwork has been used. The shadowed areas were

put to corresponding classes on the basis of ground knowledge. From the rectified image,

the area of interest i.e. the Meenachil River Basin, is extracted using the corresponding

GIS layer. Supervised classification was carried out to prepare the landuse map. For this,

initial identification of various landuse classes was carried out on the image. The

registration and digitization of the watershed was done using AutoCad 2000 to create

landuse coverage. Six landuse categories i.e. agriculture, urban, fallow, water, riverbed

and wasteland were identified. Landuse/Land Cover map of 1967 was prepared from

toposheets while those of 1996 and 2009 were prepared from the satellite imageries based

on ground observations.

1.3 Results and discussion The landuse and land cover changes in Meenachil River Basin are shown in Table 7.2

and Fig. 7.1-7.3. Since the 1900s, the extent of the basin has changed enormously (Vincy

et al., 2010). Until the year 2013, the agricultural area comprised of rice fields, palm,

arecanut, seasonal crops, etc. decreased significantly, while rubber plantations increased.

For the other landuse/cover types such as river channel and barren rock were not that

significant. In the basin, during the period (1967-1996), land with mixed vegetation

(44%) dominated followed by the area under agriculture. During the period (1996-2013)

area under plantation dominated followed by land under urban centers. From 1967 to

2013, a large part of the basin was replaced by rubber plantations (774.84%). A minor

part of the total basin in 1967 was converted into mixed vegetation, rubber plantations

and new urban settlements (Table 7.2).

In 2013, the area under rubber plantations (62.19%) remained as the major

landuse type in the basin, followed by mixed vegetation (14.83%) at the second position.

Agricultural expansion was the prime reason for deforestation (Fig. 7.1-7.3). Material

extraction and overgrazing also contributed to deforestation. In over all, the area under

the rice fields consistently showed a trend of decline. On the other hand a positive trend

of growth in urban centers in the basin was observed during the study period (Table 7.2).



Fig. 7.1: Landuse/land cover map of the study area in 1967 (Source: SOI Topographical Map, 1967)



Fig. 7.2: Landuse/land cover map of the study area in 1996 (Source: IRS-IB-LISS II acquired on March 5, 1996)



Fig. 7.3: Landuse/land cover map of the study area in 2013 (Source: IRS-P6-LISS-III data acquired on February 9, 2004 and

IRS-LISS-III data acquired in 2007)



Table 7.2: Meenachil river landuse/land cover changes (in km2)

Landuse category Area in km2 Percentage of total area

Change km2 % km2 %

1967 1996 2004 1967 1996 2004 1967-1996 1996-2004 River 23.94 23.94 23.94 1.98 1.98 1.98 - - - - Settlements 4.4 31.94 36.36 0.37 2.64 3.02 27.54 2.28 4.42 0.37 Paddy fields 189.85 88.52 24.63 15.71 7.33 2.04 -101.33 -8.39 -63.89 -5.29 Rubber plantation 50.45 287.27 774.84 4.18 23.77 64.14 236.82 19.6 487.57 40.36 Tea plantations 2.67 1.55 0.2 0.22 0.13 0.02 -1.12 -0.09 -1.35 -0.11 Mixed vegetation 895.76 690.72 250.54 74.13 57.17 20.7 -205.04 -16.97 -440.18 -36.43 Seasonal crops 0.94 - - 0.08 - - - - - - Palm 26.66 15.46 14.67 - 1.28 1.21 - -0.93 -0.79 -0.07 Barren rock 3.58 3.58 3.58 0.3 0.3 0.3 - - - - Grasslands 9.95 6.08 2.82 0.82 0.5 0.24 -3.87 -0.32 -3.26 -0.27 Cleared area - 20.25 11.28 - 1.68 0.93 - - -8.97 -0.74 Water filled area - 10.17 12.93 - 0.84 1.07 - - 2.76 0.23 Fallow land - 11.48 31.74 - 0.95 2.63 - - 20.26 1.68 Settlement/mixed crops - 17.24 20.67 - 1.43 1.72 - - 3.43 0.28

Total 1208.2 1208.2 1208.2 100 100 100 - - - -



Figure 7.4: Histogram showing landuse/land cover variation in 1967 and 1996

0

100

200

300

400

500

600

700

800

900A

rea

(sq.

km)

Landuse category

1967

1996



Figure 7.5: Histogram showing landuse/land cover variation in 1996 and 2013

0

100

200

300

400

500

600

700

800A

rea

(sq.

km)

Landuse category

1996

2013



The mixed vegetation cover in the basin showed a drastic decline during the entire

period of the study period (Figure 7.4-7.5). While the agricultural area in the basin

showed a notable fall in the respective years of study (Figure 7.1-7.3). The urban area in

the basin showed a consistent increase throughout the study period (Figure 7.1-7.3).

According to Ziegler et al. (2009), homogeneous monocultures with myriad

negative environmental consequences have emerged in the case of rubber. Erosion has

accelerated and stream sediment loads have increased where repetitive cultivation is

performed on steep slopes without appropriate conservation methods; permanent

conversion of hill slopes and road building has increased the risk of landslides; irrigation

of cash crops in the dry season has desiccated streams; and use of pesticides and

fertilizers to sustain commercial agriculture has reduced water quality. The decrement in

the area under mixed vegetation during 1967-1996 in Meenachil River basin can be

attributed to the unrestricted expansion of rubber plantations in the major portion of

Meenachil river basin which could have devastating environmental effects (Fig. 7.4-7.5).

It is reported that during 1967-2004, the area under rubber plantation has

increased by 40.36% (Vincy et al., 2010). Presently, rubber plantations cover about

62.19% of the total geographical area of the basin. Such expansions happened mainly in

the highland areas, since these areas were less occupied than the mid and lowland areas

of the state. The conversion of wetland agriculture to more gainful (at that time)

plantation crops particularly coconut and arecanut was also happening during the same

period due to the social and economic shifts that happened in the country as well as in the

state (Eapen, 1999; Raj and Azeez, 2009).

The initial growth and later decline in area under plantation can be correlated with

the social, political and economical shifts in the state as well as in the country. During the

early nineties with the liberalization of the country’s economy the planters and

agriculturists in Kerala faced huge financial crunch with the crash in the market price of

their products (Sunil, 2007). For example, import of coconut oil for industrial uses and its

culinary cheaper substitute the palm oil from East Asian countries was a blow to coconut



farmers, while freely available and low-cost spices and allies and rubber was a blow to

other farmers.

The sharp out growth in urban centers in the basin in 1967 and 1996 was related

to the declining mixed vegetation area and in the later years to the decline in area under

agricultural wetlands. According to 1981 census the basin had a population of 2 million

people, which has increased to 4.6 millions in 2001 (Census of Indian, 2001a, b, c, d).

Deforestation processes in other parts of the state also are found correlated with

population growth and infrastructure development (Chattopadhyay, 1985). During the

late 90s wetland agriculture and even plantations crops were losing their attractiveness in

the basin, as was the case in several other parts of Kerala (Raj, 2003; Raj and Azeez,

2009; Mahesh, 2000). According to Eapen (1999) urban agglomerations or out growths

were rare in Kerala till 1981 while later on their number have been almost doubling every

decade. The recent census shows that 25% of the total population of the state comes

under urban category, much closer to the national statistics (27.8%). Meanwhile people

were also abandoning their agriculture/plantations due to inadequate returns. However,

the sudden rise in the real estate market attracted lots of people to invest money in

building construction as well in tourism ventures (Raj and Azeez, 2009). For the high

demand for building construction, largely residences, many low lying lands and wetlands

at several locations nearby the main river as well as its tributaries are getting filled up and

converted.

Since the real estate sector is believed to provide much higher annual return on

investment, ranging 10-12%, compared to other investments (Mahurkar and Senthil,

2004), it attracts the resident and nonresident Keralites more or less equally. The growing

demand for real estate investment is reflected in the bench mark price for land fixed by

the state government in March 2010, that reaches up to Rs. 50/-lakhs per cent (~Rs.

10000/sq feet, reaching up to the rate in some of the well developed cities in the country)

in certain areas, an incredible level of land cost. Real estate is also believed to be a safe

long term investment among all sections of the society who has additional surplus income

to save. Moreover, it is highly lucrative for the middlemen and the promoters of real



estate ventures who orchestrate and boost up the market value of land. Conversion of

wetlands to households is a usual practice in Kerala. Most of the agriculture belts of

Palakkad have got legally converted as housing plots prior to the Land acquisition

(amendment) bill (2007). The new ‘Regulatory Framework for Conservation of Wetlands

(2008) by the central government also does not affirm the future of rice paddies, an

ecosystem on its own supporting a range of species and offering a range of ecological

services, although it deter filling up wetlands for other uses.

Urbanization always involve growth of infrastructure; buildings, roads,

communication facilities etc. In the state of Kerala the road network is growing up in

rapid pace connecting in fact all the individual houses/residences, although the roads are

not much improved in terms of their quality. The road density of the overall state is 374.9

km/100 km2 and far ahead of the national average (74.9 km/100 km2). Road development

is the single most critical factor that opens up any ecosystem or traditional rural setup for

rapid changes. Infrastructure development demands considerable lands to be divested

from its former/original use and relegation from ecologically important area to an

ecologically insignificant one.

Recent analysis of the river basin shows changes in climate particularly rainfall

(Table 2.2). This shows a statistically significant decrement in the total amount of water

flow in the river Meenachil. The present study in this context is documenting the

significant physiographic changes happening in Meenachil River basin during the last

four decades. The study along with all other related works on the river basin emphasize

the need for a scientific management plan for sustainable development of the Meenachil

River basin, keeping guard of its ecological setup, environmental resources and

ecological services.

1.5 Conclusion Consideration of the existing socio-economic scenario is necessary before implementing

any sort of landuse practices in the study area in the future. It is expected that the findings

of the investigation will undoubtedly be of use to planners and local bodies to implement

suitable landuse plans in the Meenachil River Basin, thereby achieving eco-preservation



and also enabling the restoration of degraded land units to the maximum possible extent.

The major finding of this study is that rubber plantations are smothering out other

vegetation in the area, and the cost of this monoculture to the biodiversity of the area

needs investigation. Recommendations on the basis of this study include (1) conversion

and reclamation of paddy fields should be avoided, (2) sustainable utilization of land

resources should be practiced (3) crop rotation should be implemented to improve soil

fertility (4) crops should be selected based on soil properties. Local people must be made

aware of the consequences of conversion of paddy fields, land and water management

activities must be conducted only after detailed landuse planning, sand mining from

rivers should be regulated and further expansion of rubber plantation at the expense of

other crops and large-scale cultivation in hilly areas should be discouraged. UN

Millennium Development Goals for the earth as a whole may be achieved by such small

scale steps that cumulatively would enable us to live a better, environmentally sustainable

life in cooperation with the flora and fauna of the earth.

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