Analysis of spatial patterns of forest...

Mapping and Quantitative Assessment of Vegetation of Jiribam Sub-Division, Imphal East District, Manipur, India using Remote Sensing and GIS

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Chapter

Analysis of spatial patterns of forest fragmentation

Introduction

Review of Literature

Methodology

Results and Discussion

Conclusion

References

5


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5.1 Introduction

Landscape ecology examines spatial variation in fragmentation and includes the

biophysical and societal causes and consequences of landscape heterogeneity. Human

interventions are an important influence on landscape pattern and landscape ecology. A

landscape is defined as a heterogeneous land area composed of a cluster of interacting

ecosystems that is repeated in similar form throughout (Forman and Godron, 1986). A

number of landscape indices (or metrics) that describe the landscape configuration and

composition can be formulated either in terms of the individual patches or of the whole

landscape. These metrics are used to analyze landscape structure for a wide variety of

environmental applications. The size of a patch is one of the obvious, but yet an

important characteristic of the landscape. Land use and land cover is a fundamental

variable that impacts forest fragmentation and isolation of habitats, which is being linked

with human and physical environments. While the importance of human activities is

widely recognized, the relative influence of human activities on environmental factors is

less understood. Land cover maps indicate only the location and type of vegetation and

further processing is needed to quantify and analyze forest fragmentation.

Expanding human population has caused increased resource exploitation and alteration

of land cover pattern. Anthropogenic pressure on natural resources leads to illicit cutting

of forest trees leading to deforestation which is occurring at an alarming rate (Whitmore,

1997). Human encroachment into forested regions diminishes the total forested land area.

Tropical deforestation is responsible for massive species extinction and affects biological

diversity in three ways viz. habitat destruction, fragmentation and creation of edge effects

within a boundary zone between forest and deforested areas (Roy et al., 2002). Forest

fragmentation occurs when large continuous forests are divided into smaller blocks by

function as a habitat for many plant and animal species. It also re

effectiveness in performing other ecological functions, such as water cycling and air

purification. As a large habitat becomes fragmented, all that is left are disjointed

fragments of varying size. Landscape analyses are becoming increasingly important for

biodiversity conservation (Roy and Tomar, 2000; Reddy et al., 2013).


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Remote Sensing (RS) and Geographic Information System (GIS) are now providing new

tools for advanced ecosystem management (Wilkie & Finn, 1996). Satellite images and

GIS techniques are permitting the quantification of various amounts of fragmentation

(Kharuk et al., 2004; De and Tiwari, 2008). The present chapter has attempted to

examine spatial patterns of forest fragmentation in Jiribam Sub-Division of Imphal East

district, Manipur and assumes significance, in view of using very high resolution data in

mapping of land cover features.

5.2 Review of Literature

A majority of the research on forest fragmentation is primarily focused on animal groups

rather than on tree communities because of the complex structural and functional

behaviour of the latter. There is a growing interest in analyzing and monitoring forest

fragmentation. There are few studies in India which deal with quantified fragmentation

and its impact on species diversity in northeast India (Roy and Tomar, 2000), Vindhyans

(Jha et al., 2005) and eastern Himalayas (Behera, 2010).

Roy and Joshi, (2001) did a general study on the fragmentation of the natural landscape

of Himalayas and biodiversity conservation. Their study presents the landscape approach

for characterizing the complexity of landscape boundaries by remote sensing in the

North East India. Landscape analysis showed that the indices of shape, richness and

diversity provided an additional evaluation of land cover spatial distribution within the

complex mountain landscape. The landscape analysis has provided an outline of the

degree of propagation of the disturbance from the non-biotic sources and fragmentation.

It is revealed that fragmentation has caused loss of connectivity, ecotones, corridors and

the meta population structure.

Southworth et al., (2002) has studied the landscape fragmentation by incorporating

landscape metrics into satellite analyses of land-cover change in the mountains of

Western Honduras, Central America. Landsat TM imagery from 1987, 1991 and 1996

were used in their study. Landscape metrics were calculated using the software

FRAGSTATS 2.0. With 15 20% of the land cover changing across each two-date

period, the study landscape was very dynamic. Areas of reforestation were significantly

larger than areas of deforestation, across all dates. Patch size was a good indicator of

economic activity. Stable patches of forest and agriculture were fewer and larger,


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compared to forest regrowth and clearing. Small patches of swidden agriculture were

found close to roads, at lower elevations and on more gradual slopes between 1987 and

1991. Between 1991 and 1996, expansion of export coffee production resulted in forest

clearings on steeper slopes and at higher elevations. Results highlight the importance of

landscape metrics in monitoring landcover change over time.

Armenteras et al., (2003) studied the Andean forest fragmentation and the

representativeness of protected natural areas in the eastern Andes, Colombia. Ecosystem

mapping was carried out by visual interpretation of false color digital satellite imagery

(12 Landsat TM scenes) corresponding to the following years: 1989, 1991, 1992, 1994

and 1996. They used ERDAS Imagine, Arcview and FRAGSTATS software.

Fragmentation parameters such as patch size, patch shape, number of patches, mean

nearest neighbor distance and landscape shape index were analyzed. It was observed that

Andean, sub Andean and dry forests are highly fragmented ecosystems but there is a

clear latitudinal gradient of fragmentation. De and Tiwari, (2008) estimated patchiness of various forest types in Rajaji-Corbett

National Parks and adjoining areas, Uttarakhand using remote sensing and GIS

techniques. They used LISS III data of April 1998 and were digitally processed using

ERDAS Imagine software. Patchiness of various vegetation types was estimated using

BioCAP. The highest number of patches were observed in the moist deciduous forest

(759) followed by dry deciduous forest (510). Pine and oak forests had the least number

of patches. The corridor forest had more patches per sq.km. (0.07) than the total study

area (0.04) and hence, was more fragmented.

Reddy et al., (2008) did the vegetation cover mapping and landscape level disturbance

gradient analysis in Warangal district, Andhra Pradesh, India using satellite remote

sensing and GIS. They also used LISS III data and processed using ERDAS Imagine

software. For the landscape analysis SPLAM (Spatial Landscape Analysis Model)

program was used. Disturbance index has been computed by linearly combining

fragmentation, porosity, interspersion, juxtaposition and proximity of road and

settlements. Of the eight natural forest types, moist deciduous forests have shown low

fragmentation (78.40% of area). Overall disturbance gradient analysis indicates 52.74%

of the total forested areas are under low disturbance, followed by 28.04% under medium


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and 19.22% under high. The present approach of disturbance gradient analysis provides

insight into the disturbance status of forest which is useful for forest management.

Reddy et al., (2009) did the assessment of large scale deforestation in Nawarangpur

district, Orissa, India using remote sensing and GIS. Three different satellite images from

Landsat Multi Spectral Scanner (MSS), Landsat Thematic Mapper (TM) and Indian

Remote Sensing (IRS) P6 (Resourcesat-1) Linear Imaging Self Scanner (LISS) III were

used to assess the deforestation and land use land cover change in the region for the time

period of 1973 to 2004. ERDAS Imagine, ArcGIS, SPLAM and FRAGSTATS software

were used in their study. From 1973 to 1990, more than 888.6 km2 of dense forest (rate

of deforestation = 3.62) and from 1990 to 2004, 429.7 km2 (rate of deforestation = 3.97)

were found to have been deforested.

Munsi et al., (2010) has been analyzed the landscape characterization of the Forests of

Himalayan Foothills. Changes in the landscape were analyzed using satellite data of

Landsat TM for 1990, Landsat ETM for 2001 and IRS-P6 LISS III data for 2006. They

used ERDAS Imagine, ArcGIS and FRAGSTATS software in their study. The

vegetation type maps of Dehradun forest division were prepared by supervised

classification technique in order to study the landscape dynamics. Patch density, edge

density, shape index, cohesion index, interspersion and juxtaposition index, normalized

entropy, and relative richness are some important landscape metrics used for quantifying

the characteristics of landscape. The landscape metrics analysis and transformation

analysis show that the forested areas are getting degraded and physical connectedness

between the patches have also decreased making them isolated.

Giriraj et al., (2010) has been evaluated forest fragmentation and its tree community

composition in the tropical rain forest of Southern Western Ghats (India) from 1973 to

2004. They found the area under fragmentation in the evergreen forest type showed

significant changes. Patch characteristics of 1973 were significantly different in terms of

size, proportion, shape, and context from those of 2004 because of type transition like

evergreen to semi-evergreen, expansion of Ochlandra and orchards. The patch size and

distribution for the period of 1973 2004 shows a relative decrease in the number of

smaller patches and an increase in the number of larger patches in the evergreen as well

as the semi-evergreen type.


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5.3 Methodology

Based on the LULC map obtained (objective 1) and with the support of GIS, an analysis

of landscape was undertaken. Landscape analysis has been carried out using

methodology adopted by Roy and Tomar, (2000). Spatial Landscape Analysis Model

(SPLAM) developed at Indian Institute of Remote Sensing (IIRS), Dehradun was used

(IIRS, 2002). SPLAM is a program generated for the analysis of porosity, interspersion,

fragmentation, juxtaposition, terrain complexity and disturbance index. However,

SPLAM was used for fragmentation modeling in the present study. SPLAM uses a

generic binary image as the input and the output is also written in the same format.

A grid cell of n x n (n=250 m) was used to study the fragmentation levels. Fragmentation

analysis was carried out by recoding all the forested classes and non-forest classes,

resulting in a new spatial data layer. Fragmentation was computed as the number of

patches of vegetation per unit area. A user grid cell of n x n (n=250 m) was convolved

with the spatial data layer with criteria of deriving number of vegetation patches within

the grid cell. Using a moving window approach an output layer with patch numbers was

derived and a look-up table (LUT) associated with this was generated, which keeps the

normalized data of the patches per cell in the range from 0 to 10. The mathematical

representation of the fragmentation is:

Frag = f(nF / nNF)

where, Frag = fragmentation; n = number of patches; F = forest patches; NF = non-forest

patches.

Pixels having fragmentation index values of 1 were categorized as low fragmentation;

medium fragmentation was assigned to pixels having a value of 2. All the pixels having

values from 3 to 10 were categorized as high fragmentation areas.

In order to have an estimation of the level of isolation of the forest fragmentation,

patches were categorized under five classes i.e. Very Small (<25 ha), Small (25-50 ha),

Medium (50-100 ha), Large (100-200 ha) and Very Large (>200 ha). Then the number of

forest patches falling under each class was quantified and analyzed across spatial data of

forest cover. The most relevant indices have been analyzed as per McGarigal &

Cushman, (2002) and Munsi et al., (2010).


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The eight landscape metrics such as number of patch, mean patch size, perimeter to area

ratio, patch density, edge density, mean patch edge, largest patch index, and fractal

dimension index were calculated.

Brief descriptions of the analyzed metrics are:

Number of patches (NP): It is the total number of patches in the class. Number of patches

is probably most valuable, however, as the basis for computing other, more interpretable,

metrics.

Mean Patch Size (MPS) of forest (ha): It is the average of patch size in hectares. This is a

simple and common forest fragmentation index with lower MPS indicating greater

fragmentation. It is obtained as the arithmetic mean of the areas of the forest patches.

Perimeter to area ratio (P/A): This is a simple measure of patch shape. This measure is

often standardized so that the most compact possible form, either square or circle, is

equal to 1. Higher perimeter value indicates increase of edge effect, an ecologically

undesirable influence on most species population and communities.

Patch density(PD)/100 ha: Patch density has the same basic utility as number of patches

as an index, except that it expresses number of patches on a per unit area basis that

facilitates comparisons among landscapes of varying size. Patch Density equals the

number of patches in the landscape, divided by total landscape area (m2) and multiplied

by 10,000 and 100 (to convert to 100 hectares).

PD= N/A x (10000)(100)

N= Total number of patches in the landscape

A= Total landscape area (m2)

There is a direct correlation between patch density and degree of disturbance. Higher the

value of patch density (PD) higher is the disturbance magnitude and vice versa.

Edge Density (ED): It is the sum of length of all edge segments for the class, divided by

total landscape area. It is a measure of landscape configuration. It gives edge length on a

per unit area basis that facilitates comparison among landscapes of varying size.

Largest Patch Index (LPI): It is the percentage of total landscape area occupied by the

largest-sized forest patch. It is a simple measure of dominance (McGarigal, 1994). If a


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landscape contains one large patch occupying a large amount of the total landscape area,

that patch may have a dominant and important role in the function of the entire

landscape.

Fractal-Dimension ((FD) Index: Fractal dimension has been used for measurement,

simulation and as a spatial analytic tool in the mapping sciences. The fractal dimension is

an index of the complexity of shapes on the landscape. If the landscape is composed of

simple geometric shapes like squares and rectangles, the fractal dimension will be small,

approaching 1.0. If the landscape contains many patches with complex and convoluted

shapes, the fractal dimension will be large.

5.4 Results and Discussion

Patch size stratification of forest was considered as a primary criterion to assess the

fragmentation. Each index indicates one aspect of fragmentation, the number of patches

might indicate that it suffers a higher rate of deforestation. Nevertheless, information on

the number of patches alone does not have any interpretive value because it has no

information about area, distribution or shape of the fragments (McGarigal and Marks,

1994). Therefore this index was calculated together with other metrics that could

together be more interpretable. Another example is the mean patch size index.

Progressive reduction in the size of ecosystem fragments is a key component of

ecosystem fragmentation. Thus a landscape with a smaller mean patch size for the target

ecosystem than another landscape might be considered more fragmented (McGarigal and

Marks, 1994).

Landscape indices provided a useful tool to explore within site variability. The use of

class-level landscape pattern indices enabled assessment of the spatial configuration of

forest cover. Analysis of spatial landscape pattern reveals that different land cover types

shows representation of total 801 patches (Table 11). Percentage of forest cover indicates

that forests are the predominant land cover type (67.3%) followed by built up area and

agriculture. At landscape level forests possess highest proportion of patches (41.7%)

followed by built up area, agriculture and wasteland.


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Table 11. Spatial accounting of land use/land cover.

Class Area-ha % of area No. of patches % of patches

Forest 11819 67.3 334 41.7 Agriculture 2327 13.3 134 16.7 Built up area 2577 14.7 164 20.5 Wasteland 419 2.4 105 13.1 Water Bodies 390 2.2 40 5.0 Other land use 29 0.2 24 3.0 Grand Total 17561 100 801 100

The indices of Largest Patch Index (LPI), Number of forest patches (NP) and Mean

Patch Size (MPS) correspond to area metrics. The MPS was estimated as 35.4 ha. It was

very less as compared to Nawarangpur district of Orissa which has evidenced large scale

deforestation and accounted for higher annual rate of deforestation of -3.2 (Reddy et al.

2009). Edge Density (ED) was found to be very high. This indicates influence of

anthropogenic impact on edge to core/interior forest systems. Increased amount of forest

edge in the study area is attributed to due to prevailing shifting cultivation. It plays a key

role in the distribution of native species. Patch density index / 100 ha show the extent of

fragmentation of forest class and estimated as 0.0002. Largest patch index of forest at

landscape level was estimated as 7.4. It points out that forest is the predominant land

cover contributing for moderate level largest patches. In the present study, largest patch

size for forest shows clear evidence of the increasing pattern of biotic pressure in terms

of deforestation and degradation. The mean patch edge can be considered as baseline

indicator to monitor changes in spatial configuration of forest. The measured fractal

dimension of 2.6 in Jiribam is indicative of very irregular terrain conditions.


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Table 12. Landscape metrics for forests of Jiribam Sub-Division.

Sl.no. Landscape metrics Value Patch density and size metrics

1 No. of patches 334 2 Mean Patch Size of forest (ha) 35.4 3 Patch density/100 ha 0.0002

Edge metrics 4 Edge Density (m/ha) 189

5 Mean Patch edge (m) 2743 Shape metrics

6 Perimeter to area ratio 0.013 7 Largest Patch Index (%) 7.4 8 Fractal-Dimension Index 2.6

The size class distribution of number of forest patches and area of patches was depicted

in Table 13. Of the total 334 forest patches, 234 patches belonged to <25 ha patch size

category contributing to an area of 16.9 sq.km and proportionately contributes 14.4% of

total forest area. The patch class of >200 ha represented only 13 patches (Fig. 13.1). It

indicates higher level of human disturbance on forest habitats of Jiribam Sub-Division.


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Table 13. Size class distribution of forest fragments.

Sl. No. Patch class Area-ha % of area No. of patches % of patches

1 <25 ha 1696.8 14.4 234 70.1 2 25-50 ha 1304.6 11.0 37 11.1 3 50-100 ha 1962.5 16.6 30 9.0 4 100-200 ha 2579.6 21.8 20 6.0 5 >200 ha 4275.1 36.2 13 3.9 Grand Total

11818.7 100 334 100

Fig. 13.1. Line chart shows representation of various Forest fragments.


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Fig. 13.2. Fragmentation map of Jiribam Sub-Division.


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Fig. 13.3. Distribution of Fragmentation in Jiribam Sub-Division.

The fragmentation levels were categorized into high (H), medium (M) and Low (L) areas

(Fig 13.2). Moderate fragmentation area dominates the landscape of Jiribam Sub-

Division occupies 40% of area followed by high (33.5%) and low (26.5%) (Fig 13.3).

The present analysis supported the conclusion of several authors that forest

fragmentation tends to increase the number of patches and decrease the mean patch size.

Midha and Mathur, (2010) have found that a class with greater density of patches could

be considered more fragmented. In the present study also forest is representing more

number of patches which indicate current status of high fragmentation. Overall landscape

evaluation infer that study area is composed of various man made classes and affects the

naturalness of forest ecosystems through edge effect, isolated small patches, invasion of

alien species, shifting cultivation and proximity of plantations and settlements. The

present work has provided regional pattern of forest fragmentation. The current

landscape scenario is characterised by high natural habitat cover (67.3%) but fragment

size distribution strongly distorted towards small values (patches of less than 25 ha).

Many of the landscape level studies carried out in India have used IRS LISS III and IRS

AWiFS data and spatial accounting was done at 1:250,000 scale (Reddy et al., 2012).

The uniqueness of the study lie in the spatial analysis of fragmentation in fine spatial

scale (i.e. 1:25,000) based on high resolution IRS LISS IV satellite data.


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5.5 Conclusion

animals, air, water and soil within a relatively homogenous spatial unit. Landscape

between the various

spatial units. In landscape ecology patch characteristics are important indicator for

disturbance gradient analysis. The most remarkable characteristics of patches are their

size and area. The landscape analysis combines satellite remote sensing data along with

GIS and in-situ observation in the study of management, and conservation of natural

resources.

Forest fragmentation is considered as one of the greatest threats to global biodiversity

because the forests are the most species-rich of terrestrial ecosystems. The present study

using remote sensing based analysis of forest fragments could play a major role for

formulating policies for conserving native vegetation. There is an urgent need for

rational management of the remaining forest if it is going to survive beyond the next few

decades. It is the need of the hour to define political and conservation actions that

minimize the impact of human activities on the remaining native forests. The description

of landscape spatial pattern provides a basis for future research investigating such

impacts.

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