Spatial statistical analysis for delineating timber ... · Keywords: GIS, Spatial statistics,...

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 2, No 2, 2011

© Copyright 2010 All rights reserved Integrated Publishing services

Research article ISSN 0976 – 4380

Submitted on October 2011 published on November 2011 655

Spatial statistical analysis for delineating timber species diversity hotspots

at compartment level Handique B.K

1, Gitasree Das

2

1- North Eastern Space Applications Centre, Umiam, 793103 Meghalaya, India

2- Department of Statistics, North-Eastern Hill University, Shillong, 793022 Meghalaya

[email protected]

ABSTRACT

Loss of biodiversity due to rapid deforestation has been a major concern for conservation

biologists. Even though biodiversity hotspots are identified at global scale, methods for

identification of biodiversity hotspots at lower administrative units are missing. In this paper,

a novel technique using remote sensing, geographical information system and spatial

statistical analytical tools has been demonstrated for delineating timber species diversity

hotspots within a selected reserve forest. Even though, the whole reserve forest is believed to

be rich in timber species diversity, only 23 percent of the reserve forest has been found to be

covered under timber species diversity hotspots. Among three major forest types, moist

deciduous forest type found to contribute highest towards timber species diversity. Positive

correlation of timber species diversity with canopy density has been observed. Among

different elevation levels, 500-700 MSL has maximum areas under timber species diversity

hotspots. This approach of identification of hotspots within a reserve forest is expected to

help in optimal decision making for timber harvesting and in situ conservation planning.

Keywords: GIS, Spatial statistics, diversity index, G-statistics, hotspot, coldspot

1. Introduction

Monitoring of tree diversity and forest structure is a key requisite for understanding and

managing forest ecosystems. Biodiversity indicators help to establish and monitor levels of

biodiversity in different ecosystems such as forest ecosystem. The number of these indicators

is vast and they range from gene to landscape pattern depending on spatial scales (Motz and

Pommerening, 2010). These indicators must provide tangible goals for forest policy and other

relevant stakeholders (Geburek et al., 2010). There have been many studies on biological

diversity and richness of Indian forest conditions at different spatial scales using satellite

remote sensing and Geographical Information System (GIS). (Amarnath et al., 2003; Menon

and Bawa 1997; Murthy et al., 2003; Murthy et al., 2006; Navalgund et al., 2007; Salem,

2003) Studies were also carried out to monitor the process of forest fragmentation which

leads to loss of biodiversity (Behera, 2010; Cayuela et al., 2006; Goparaju and Jha, 2010; Jha

et al., 2005). Satellite remote sensing data as an important tool to generate spatial data on

different forest parameters have been used to provide stratification base for optimal ground

sampling to estimate different forest resources (Udaya Lakshmi et al., 1998).

Numerous works have been carried out for characterization and conservation planning of

biodiversity hotspots with satellite remote sensing and GIS inputs (Behera and Roy, 2010;

Nandy and Kushwonservaha, 2010; Natarajan et al., 2004). Although remote sensing

imageries have greatly enhanced our ability to monitor biodiversity at global scale but we

lack methods to delineate areas with high levels of biodiversity at local level without

Spatial statistical analysis for delineating timber species diversity hotspots at compartment level

Handique B.K, Gitasree Das

International Journal of Geomatics and Geosciences

Volume 2 Issue 2, 2011 656

intensive and time-consuming ground surveys (Bawa et al., 2002). Most of these exercises

carried out at coarse geographical scales are not able help forest planners and decision makers

for appropriate decision making at lower administrative level (Prasad et al., 1998). For

example, Eastern Himalayan region is considered as one of the rich biodiversity hotspots at

global scale. But for conservation planning by local administration, biodiversity hotspots

need to be delineated within smaller administrative level such as at forest compartment level

within a reserve forest. (Kati et al., 2004; McGlincy, 2005). Delineation of these hotspots

should be based on suitable quantitative approach with sound statistical methods. Moreover,

we may categorize the diversity in terms of different forest categories such as timber species,

medicinal plant species, fuel and fibre tree species or other important shrubs and herbs. This

will help in different strategic decision making for better management of the forest resources.

Spatial statistics analytical techniques with advanced GIS software help in analyzing the

spatial order and association of multi-dimensional indicators (ESRI, 2005). Analysis of order

and association of different spatial features helps in studying the clustering pattern of the

spatial features based on their attributes (Jeremy et al., 2002). In a forest ecosystem, species

abundances are positively autocorrelated; such that nearby points in space tend to have more

similar values than would be expected by random chance (Walter, 1998). Spatial statistical

analysis will help to identify the priority zones based on spatial distribution and abundance of

timber species and would lead to identification of timber diversity hot spots and cold spots.

An earlier study carried out by Handique and Das, 2007 demonstrated the potential

application of GIS and spatial statistical analysis to categorize timber species richness

hotspots for optimal harvesting and conservation planning. It was also emphasized the need

of studying species diversity and disturbance indices for delineating the hotspots.

The objective of this study is to demonstrate the potential application of spatial statistical

analysis to prioritize timber species diversity hotspots at compartment level. Six well known

diversity indices have been employed to study the timber species diversity in a selected

reserve forest in north eastern India. A brief overview of the diversity indices used in the

study is given in Table 1.

2. Materials and Method

2.1 Study area

Langting Mupa Reserve Forest (LMRF), the biggest reserve forest in the north eastern region

of India with an area of 498 Sq km. has been selected for the case study. For administrative

management the reserve forest has 91 compartments along with two regions put under

unclassified state forest (USF). The reserve forest extends from latitude between 250 19

/ 58

//

N and 250 46

/ 53

// N and longitude between 92

0 56

/ 43

// E and 93

017

/ 07

// E. Altitude ranges

between 80 to 880 meters above MSL. Location map of the study areas is given in Figure 1.

Bamboo mixed type of formation is dominant in this reserve forest and the formation occurs

due to past shifting cultivation practices in the area. The bamboo forests occur either mixed

with broad leaved species or in pure form. Other forest formation of the reserve forest is

moist semi-evergreen forest and east Himalayan moist-mixed deciduous forest. According to

earlier studies, the reserve has been found to be rich in terms of species diversity (IIRS, 2002;

NESAC, 2007). But over exploitation of forest products and encroachment has become major

threat to the reserve forest and immediate attention is required in terms of conservation

planning for timber richness and diversity (Sarma et al., 2009).





Figure 1: Location map of the study area

2.2 Satellite image processing

Satellite image processing is required for delineating suitable strata for allocation of sample

points for collection of timber inventory data. A combination of forest types and crown

density is made for delineating the strata. Different forest types of the reserve forest have

been delineated using Indian Remote Sensing (IRS) P-6 Linear Imaging and Self Scanning

(LISS) III satellite imagery with spatial resolution of 23.5 meter following maximum

likelihood classification algorithm (Jensen 1999; Lillesand and Keifer 2003). Two season

satellite images during October, 2009 and February, 2010 were taken so that deciduous forest

areas can properly be delineated. Four forest types have been delineated from satellite

images namely semi-evergreen, moist deciduous, bamboo mixed and plantations. Pure

bamboo areas and plantation areas have not been considered for allocation of sample points.

Five forest canopy density classes in terms of percent canopy cover have been delineated

using Cartosat 1 satellite data having 2.5 meter resolution based on visual interpretation as

<20%, 20 – 40%, 40 – 60%, 60 – 80% and >80%. Density classes have not been delineated

for the bamboo mixed areas. Density class 80-100% has not been found in the reserve forest.

Forest density class 60-80% has not been found in semi-evergreen and moist deciduous forest

type. As such nine strata have been considered for allocation of sample points in proportion

to the area under different strata.

2.3 Determination of timber species diversity hotspots using spatial statistics

A total of 285 sample points have been allocated based on a pre-inventory exercise conducted

to estimate the variability in terms of timber species diversity. The field data were collected

in 31.6m X 31.6m (0.1ha) quadrant and the details of timber species have been recorded.





These data have been utilized to estimate six selected diversity indices. An overview of

diversity indices used in the study is given in Table 1.

Estimated mean and standard error of the estimates of selected indices for the reserve forest

have been calculated using the stratified random sampling procedure with proportional

allocation. (Cochran, 2002; Shiver and Borders, 1996).

For the population mean per unit, the estimate used in stratified sampling issty , where

∑∑

=

= ==L

h

hh

L

h

hh

st yWN

yN

y1

1 _______ (1)

The suffix h denotes the stratum and i the unit within the stratum.

L = total number of strata =hN total number of units (0.1 ha) in stratum h.

=hn number of unit areas (0.1 ha) in the sample. =hiy value obtained for the i th unit.

N

NW hh = stratum weight

h

hh

N

nf = sampling fraction in the stratum

and, LNNNN +++= .......21

Table 1: Overview of diversity indices used in the study

Index Variable definition Remarks

Shannon

Diversity Index

∑=

−=n

1i

ii pln pH

H is the Shannon diversity index

N

np i

i = , ‘ni’ number of individuals

belong to i species

‘N’ is the total number of individuals,

n is the no of species

It is the most preferred index

among other diversity indices

proposed by Shannon and Weaver

in 1949. The index values are

between 0.0-5.0. The values above

3.0 indicate that the structure of

habitat (forest) is stable and

balanced. Values under 1.0

indicate that there are degradation

in the forest

Simpson

Diversity Index

( )[ ] ( )1N/N1nn∆1 ii −−=− ∑

∆ : Simpson diversity index

ni: number of individual belonging to

i species

N: Total number of individuals

Simpson index values (∆) are

between 0-1. But while

calculating, final result is

subtracted from 1 to correct the

inverse proportions. (Simpson,

1949 and Begon et al. 2006).

Margalef

Diversity Index

d= (S-1)/lnN

d: Margalef diversity index

S: Total number of species

N: Total number of individuals

Margalef Diversity Index has no

limit and it shows variation

depending upon the number of

species. Thus it is used for

comparison of sites (Ludwig and

Reynolds 1988, Magurran and

Mcgill 2010).





Mclntosh

Diversity Index )]N/[NnNMc

2

i −

−= ∑

Mc: Mclntosh Diversity index

ni: number of individual belonging to

i species

N : Total number of individuals

It was suggested by Mclntosh in

1967. The values are between 0-1.

When the values getting closer to

1, it means the species in the

habitat are homogeneously

distributed.

Pielou

Evenness Index J= H /

maxH

J: Pielou Evenness Index

H :The observed value of Shannon

Diversity Index

maxH : lnS

S: Total number of species

It was derived from Shannon

Index by Pielou in 1966. The ratio

of the observed value of Shannon

index to the maximum value gives

the Pielou Evenness Index. The

value ranges between 0-1. When

the value is getting closer to 1, it

means that the individuals are

distributed equally.

Mclntosh

Evenness Index )]S/[NnNMcE

2

i −

−= ∑

McE: Mclntosh Evenness index

ni: number of individuals belonging

to i species, S: Total number of

species

N : Total number of individuals

It was derived from Mclntosh

Diversity Index. The value ranges

between 0-1. When the value is

getting closer to 1, it means that

the individuals are distributed

equally (Heip and Engels 1974)

For stratified random sampling, an unbiased estimate of the variance of sty is

( ) ( )

( )hh

hL

h

h

h

hL

h

hhhst

fn

sW

n

snNN

Nyv

−=

−=

∑

∑

=

=

1

1

2

1

2

2

12

_______ (2)

Where, h

n

i

hi

hn

y

y

h

∑== 1 is the corresponding sample mean

( )

1

1

2

2

−

−=∑=

h

N

i

hhi

hn

yy

s

h

2.4 Delineation of timber species diversity hotspot

It is of interest to see the spatial distribution of sample points characterized by different

timber species. Typically, species abundances are positively autocorrelated; such that nearby

points in space tend to have more similar values than would be expected by random chance.

In classifying spatial patterns as either clustered, dispersed, or random, we can focus on how

various points or polygons are arranged. We can measure the similarity or dissimilarity of

any pair of neighbouring points or polygons. When these similarities and dissimilarities are

summarized for spatial pattern, we have the spatial autocorrelation (Lee and Wong, 2001).

Moran’s I have well-established statistical properties to describe spatial autocorrelation

globally (Chou, 1997). However it is not effective in identifying different type of clustering

spatial patterns. These patterns are sometimes described as ‘hotspots’ and ‘coldspots’ based

on statistical significance. If high values of attributes are close to each other, Moran’s I will

indicate relatively high positive spatial autocorrelation. The clusters of high values may be

labeled as a hotspot. But high positive spatial autocorrelation indicated by Moran’s I could be

created by low values close to each other. This type of clusters can be described as coldspot.

The G statistics (Getis and Ord, 1992) has the advantage of detecting the presence of hotspots





or coldspots over the entire study area. A local measure of spatial autocorrelation is the local

version of the General G statistics. The local G statistics is derived for each aerial unit to

indicate how the values of aerial units of concern is associated with the values of surrounding

aerial units defined by a distance threshold d. The Local G statistics is defined as:

( )( )

∑

∑=

j

j

j

j

ij

ix

xdw

dG ; ji ≠ _______ (3)

This G statistics is defined by a distance d, within which the aerial units can be regarded as

neighbours of i, xi denotes the attribute value. The weight wij(d) is 1 if aerial unit j is within d,

or 0 otherwise. Thus the weight matrix is essentially a binary symmetrical matrix, but the

neighbouring relationship is defined by distance, d. Basically the numerator of (1) which

indicates the magnitude of Gi (d) statistics will be large if neighbouring features (Diversity

Index values) are large and small if neighbouring values are small. A moderate level of Gi(d)

reflects spatial association of high and moderate values, and a low level of Gi(d) indicates

spatial association of low and below average values. Before calculating the G statistics we

need to define a distance d, within which aerial units will be regarded as neighbours. In this

exercise we have defined d as a distance of 2 km based on the extent of the study area and

spatial distribution of sample points. So the sample points will be regarded as neighbours if

they are within an aerial distance of 2 km. For detail interpretation of the general G statistics

we have to rely on its expected value and standardized score (Z score).

To derive Z score and to test for the significance of the general G statistics, we have to know

the expected value of Gi(d) and its variance. The expected value of Gi(d) is

( )( )1−

=n

WGE ii ________ (4)

where,

( )dwWj

iji ∑=

The expected value of Gi(d) indicates the value of Gi(d) if there is no significant spatial

association or if the level of Gi(d) is average. Then we need to derive the Z score of the

observed statistics based on the variance. According to Getis and Ord the variance of Gi(d) is

( ) ( ) ( )[ ]22

iii GEGEGVar −= ________ (5)

where,

( )( )

( )( )( )

( )( )21

1

21

11

2

2

2

−−

−+

−−

−−

=

∑

∑nn

WW

nn

xWnW

x

GEiij

jii

j

j

i

Where, n denotes the number of aerial units (sample points) in the entire study area.

A ‘Z’ score is calculated to assess whether the observed clustering / dispersion is statistically

significant or not. The Z score is calculated as:

________ (6) ( ) ( )( )ISD

IEIOZ

−=





Based on the significance of Z score, the sample point locations have been categorized as hot

spots or cold spots. Sample point locations having Z score values > 1.96 have been

categorized as hotspots where Z score <1.96 have been categorized as coldspots.

3. Results and Discussion

The estimated mean of the selected six indices computed for the whole study area as per

stratified random sampling procedure with proportional allocation is given in Table 2.

Estimated mean of Shannon Weiner index has been found to be below 1. Similarly, estimated

mean of Margalef diversity index is just above 1, which indicates that the forest is already in

a degraded condition. Other indices, which range values from 0 to 1, estimated means have

been found to be between 0.503 to 0.597. This confirms that the timber species diversity is on

the lower side in the reserve forest. Co-efficient of variation of estimated means have been

found to be ranging between 4.188 to 5.366, which indicates that large numbers of sample

points allocated in the reserve forests could sufficiently take care the variation in terms of

diversity indices.

Table 2: Estimated mean and standard error of diversity indices for the RF

Shannon

Diversity

Index

Simpson

Diversity

Index

Margalef

Diversity

Index

Mclntosh

Diversity

Index

Pielou

Evenness

Index

Mclntosh

Evenness

Index

Mean 0.615 0.564 1.225 0.527 0.503 0.5967

( )ySE 0.033 0.024 0.061 0.024 0.026 0.025

( )yCV % 5.366 4.255 4.980 4.554 5.169 4.188

Three dominant forest types in the reserve forest were considered for allocating the field

sample points. Moist deciduous areas are showing maximum diversity in the forest as the

values of average diversity indices are found to be higher in case of all the six diversity

indices (Figure 2). Average values of all the six selected indices in case of moist deciduous

forest have been found to be significantly different (p<0.05) from other two forest types viz.,

semi-evergreen and mixed bamboo forest type.

Figure 2: Forest type wise variation of diversity indices





Among the four crown density classes, D4 class (60-80%) is showing highest diversity as

indicated by the selected six diversity indices (Figure 3). Average index values in case of

higher density classes are significantly different from the lower density classes such as

average SWI or SI values in case of 0-20% or 20-40% is significantly different from that of

60-80% (p<0.05) This indicates that the forest areas having less disturbance and with higher

crown density contribute higher timber species diversity in the RF.

Figure 3: Forest crown density wise variation of diversity indices

Elevation map of the reserve forest have been prepared with SRTM (Shuttle Radar

Topography Mission) data (Rabus 2003). Three different ranges were considered for studying

the relationship of diversity with the elevation level (viz, 0-500 MSL, 500-700 MSL and 700-

1000 MSL). It has been observed that the elevation range 500-700 MSL has the higher level

of timber species diversity, but the average index values for these 3 ranges are not

significantly different from each other (Figure 4). This indicates that the role of elevation

range considered in the study has a limited role on timber species diversity.

Figure 4: Elevation wise variation of diversity indices

The general G statistics has been calculated to delineate the areas based on whether high

values or low values of diversity indices tend to cluster in the area. In other words it will

identify the species diversity hotspots and coldspots in the study area. Highest value of Z

score for G statistics is recorded as 2.421 while lowest is -2.013. Table 3 shows the

percentage of hotspots and coldspots determined with six selected indices. Percent of

hotspots as well as the coldspots are not significantly different (p< 0.05) among the six

selected diversity indices.





Table 3: Percentage of hotspots and coldspots under different diversity indices

Index Hotspot (%) Coldspot (%) Others (%)

Shannon Diversity Index 15.44 18.60 65.96

Simpson Diversity Index 13.68 19.65 66.67

Margalef Diversity Index 14.39 21.40 64.21

Mclntosh Diversity Index 14.39 17.54 68.07

Pielou Evenness Index 13.33 17.19 69.47

Mclntosh Evenness Index 12.28 17.89 69.82

As seen in the Table 3, number of hotspots is always less than the number of coldspots in

case of all the selected six indices. This indicates that there are more clustering of low values

of diversity indices, which is an indicative of disturbed forests. We have taken the common

hotspots and coldspots shown by all the indices and given in the figure 4. A few prominent

clustering of hotspots have been observed. On the northern most part of the RF, a cluster of

hotspots are falling in compartment numbers 1, 2, 4, 9, 9a (42.35 Sq. Km). Compartment

numbers 17, 18, 24, 34, and 36 representing a cluster of hotspots, which is having an area of

27.77 Sq. Km. Dense semi-evergreen forest dominating this area is found to be least

disturbed as observed during the field study. Two more clusters of hotspots have been

observed, one covering the compartment numbers 50, 54 and 55 (19.99 Sq. Km) and another

covering the compartments 57, 68, 69, 70, 73, 74 and 78 (55.11 Sq. Km).

On the other hand, there is less clustering of coldspots in the RF. A large number of

compartments on the western side of the RF represent diversity coldspots, thereby indicating

more disturbances towards this side. As observed during the field study, western side of the

RF having less elevation and slopes are prone to encroachments. A little less homogeneous

clustering of coldspots has been observed towards southern part of the RF. It is interesting to

note that southern part of the reserve forest having higher elevation with steep slopes is less

disturbed, but still timber species diversity is less in this part (Figure 5).

It is also interesting to observe that some of the hotspots are located in bamboo mixed forests.

Majority of the cold spot have been found to be located in low forest crown density areas

(<40%) with bamboo mixed type of forest. Field verification of these areas confirmed that

extraction of timbers from these areas has resulted in secondary bamboo growth. With

luxuriant bamboo growth, which has shallow root system, hinders the growth of other timber

species. It is observed that the reserve forest is under severe threat of human encroachment

and timber felling, which has resulted in forest fragmentation and loss of timber species

diversity. About one-third of the field sample points recorded to have zero timber diversity

which indicates that the forest is highly disturbed and immediate attention is required to

maintain the diversity of this important reserve forest. Understanding the local causes of

deforestation and timber felling may be the first step towards framing realistic policies and

innovative conservation solution (Davidar, 2007).





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Figure 5: Timber species diversity hotspots and coldspots plotted on elevation

map

4. Conclusion

The study reveals that even though the selected reserve forest is believed to be rich in terms

of biodiversity as a whole, there is a wide variation in terms of timber species diversity within

the reserve forest. We have quantified the variations of timber species diversity in respect of

different physiographic conditions and forest compositions. Spatial statistical analytical

techniques along with satellite remote sensing and GIS could be efficiently utilized for

identifying the timber species diversity hotspots and coldspots within the reserve forest. In a

similar way, attempt can be made to delineate the hotspots and coldspots of other groups of

forest species such as fuel, fibre, medicinal plants, etc. Extraction of timbers and other forest

products without a proper management plan will lead to further deterioration of the reserve

forest. On the other hand, prioritization of the areas inside the reserve forest will help in

better management of the forest in terms of timber harvesting and in situ conservation plans.

Thus this approach of identification of hotspots within a reserve forest will contribute to

0 4 82Kilometers

-





resolve the debate on sustainable use versus conservation planning of forest resources. Recent

debate on conservation of mangrove forests is one such issue, where this methodology may

help in optimal decision making.

Acknowledgements

The authors would like to thank Dr. S. Sudhakar, Director, NESAC for his guidance and

encouragements. Thanks also due to officials and field staff of Forest Department, North

Cachar Hills Autonomous District Council for their sincere support in collecting forest

inventory data. Maps and other records received from NESAC publications are duly

acknowledged.

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