FOSS4G: Prediction of forest cover transitions in Uttara Kannada, Central Western Ghats

Conference Paper (PDF Available) · June 2015with 136 Reads
Conference: OSGEO-India: FOSS4G 2015 - Second National Conference "OPEN SOURCE GEOSPATIAL TOOLS IN CLIMATE CHANGE RESEARCH AND NATURAL RESOURCES MANAGEMENT” 8-10TH JUNE 2015, At DEHRADUN, INDIA, Volume: 2
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Abstract
Landscape is a mosaic of forest and non-forest elements depending on the climate, geology, land use transitions, etc. Forests constitute the vital source of natural resources, aiding in socioeconomic development with water and food security and environmental protection. Alterations in landscape structure due to unplanned anthropogenic activities have resulted in fragmentation of contiguous forests affecting the biodiversity, soil retention capacity, hydrologic regime, loss of carbon sequestration potential, etc. Deforestation has been considered as one of the driver of global warming and consequent changes in the climate. Uttara Kannada district of Central Western Ghats having six forest jurisdictions of Karnataka has been experiencing landscape dynamics during post-independence period due to implementation of large-scale developmental projects. Land use analyses using temporal remote sensing data with FOSS show the decline of forest cover from 60.45 % (2004) to 53.36% (2013), with an increase in built-up from 2.6% to 3.5%. Prediction of forest cover in 2022 is done through Markov-cellular automata (CA–Markov) helps in for inferring intensity, extent and also evolving appropriate forest management strategies. The integration of geoinformatics with freely downloadable remote sensing data has aided in planning enhancing transparency in the administration apart from economical conservation actions for sustainable management of natural resources.
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FOSS4G: Prediction of forest cover transitions in
Uttara Kannada, Central Western Ghats
Bharath Setturu 1, 2
1 Energy and Wetlands Research Group, Centre for
Ecological Sciences [CES],
2 Lab of Spatial Informatics, International Institute of
Information Technology, Hyderabad 500032, India,
settur@ces.iisc.ernet.in; setturu.bharath@research.iiit.ac.in
Rajan K S 2
2 Lab of Spatial Informatics, International Institute of
Information Technology, Hyderabad 500032, India;
rajan@iiit.ac.in
Ramachandra T V 1, 3
1 Energy and Wetlands Research Group, Centre for
Ecological Sciences [CES], cestvr@ces.iisc.ernet.in
3 Centre for infrastructure, Sustainable Transportation and
Urban Planning [CiSTUP], Indian Institute of Science,
Bangalore, Karnataka, 560012, India.
Abstract Landscape is a mosaic of forest and non-forest
elements depending on the climate, geology, land use transitions,
etc. Forests constitute the vital source of natural resources, aiding
in socioeconomic development with water and food security and
environmental protection. Alterations in landscape structure due
to unplanned anthropogenic activities have resulted in
fragmentation of contiguous forests affecting the biodiversity, soil
retention capacity, hydrologic regime, loss of carbon
sequestration potential, etc. Deforestation has been considered as
one of the driver of global warming and consequent changes in
the climate. Uttara Kannada district of Central Western Ghats
having six forest jurisdictions of Karnataka has been
experiencing landscape dynamics during post-independence
period due to implementation of large-scale developmental
projects. Land use analyses using temporal remote sensing data
with FOSS show the decline of forest cover from 60.45 % (2004)
to 53.36% (2013), with an increase in built-up from 2.6% to
3.5%. The quantification of fragmentation of forests reveals that
forest under jurisdiction of Sirsi forest division has lost major
core forest cover from 58.5% to 36.93% with an increase in non-
forest cover (crop land, plantations, built-up, etc.). Prediction of
forest cover in 2022 is done through Markov-cellular automata
(CAMarkov) helps in for inferring intensity, extent and also
evolving appropriate forest management strategies. The
integration of geoinformatics with freely downloadable remote
sensing data has aided in planning enhancing transparency in the
administration apart from economical conservation actions for
sustainable management of natural resources.
Keywords- Forest fragmentation, CA-Markov, Land use Land
cover, Central Western Ghats
I. INTRODUCTION
Landscape with a mosaic of interconnected forest and non-
forest patches constitute a complex ecological, economic and
socio-cultural systems. However, the structure of the
landscape is being altered due to the uncontrolled
anthropogenic activities such as industrialization, agriculture,
deforestation, etc. The Earth's land surface has lost 40 percent
of natural forest by 1990 due to expansion of cropland and
permanent pasture [1]. Land use land cover (LULC) changes
include the land transformation from one land use to another,
leading to land degradation with the decline of biological,
ecological and hydrologic aspects affects economic
productivity. LULC change resulting in deforestation has been
recognized as an important driver of global environmental
change. This necessitates quantification of LULC changes to
evolve sustainable natural resource management strategies.
The economic growth, industrial corridors, rapid demographic
transition and agriculture expansion are stimulating change in
forests through multiplier effects at a spatio temporal scales.
Land use change in forested landscape will have significant
impact on structure, composition of patches, which results in
fragmentation, extinction of native species, loss of
biodiversity, soil erosion, alteration of nutrient and hydrology.
Forest landscape changes subdivides the continuous native
forests to more and smaller sizes and isolated forest patches
[2, 3]. Fragmentation is stated as a process of breaking
contiguous natural forest patches into smaller tracts of forest
surrounded by other land uses, causing a disruption in
continuity of the natural landscape. Habitat fragmentation with
subsequent edge effects influence ecosystem goods and
services including carbon sequestration, hydrologic regime
and biodiversity. The edge effect will lead to often perishing
of large trees and being replaced by short-lived pioneers,
resulting in decreases in forest biomass and basal area [4]. The
edges will aggravate predation [5], fire susceptibility,
microclimate alteration and enhance carbon emissions.
The comprehensive knowledge about LULC has
become increasingly important for planning and visualization
of future growth to overcome the problems of haphazard,
uncontrolled development [6]. Temporal remote sensing data,
geographic information systems (GIS) techniques, free and
open source software technologies are providing efficient
methods for the analysis of land use and modelling of LULC
dynamics required for planning [7]. Modeling and
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visualization is considered as a conceptual, symbolic or
mathematical approach of deriving relationship between the
driving forces such as socioeconomic, political, technological,
natural and cultural factors [8] of a complex system and their
influence on landscape. Understanding the driving forces of
land use development in the past, managing the current
situation with modeling the future will help developers,
planners for managing natural resources and nature
conservation. Cellular automata (CA), Markov chain models,
agent based models and fractals approaches has evolved as a
promising tools of understanding macro to micro level
changes and comprehensive projection of a region [9, 10]. CA
combined with Markovian process can supports to derive
changes within a grid of cells and its structure based on
expanded neighborhood definitions based on deterministic
logical transition rules. The neighborhood selection (eight or
four) is considered as a vital element, which derives the
interaction between the various land use and dynamic systems
present in the landscape [11, 12]. CA-Markov (Cellular
automata and Markov process) models have modelling LULC
changes across globe. CA-Markov models been used
extensively a better theoretical understanding of the complex,
nonlinear relationships of LULC processes [13], aids in
measuring the quantity of change that is expected to achieve
through Markov Chain analysis, particularly the transition
area, probability matrices. CAMarkov model applies a
contiguity kernel to ‘produce’ a land use map to a later time
period through a CA function that converts the results of the
Markov chain to spatially explicit outcomes [14]. Defining
appropriate transition rules based on training data is the
greatest concern in CA modelling, which helps in complicated
decision-making process [15] to understand the neighboring
effects of land use activities. The objective of current
endeavor are:
(i) to analyze spatio temporal land use changes across
various forest jurisdictions of Uttara Kannada using free and
open source software,
(ii) accounting temporal fragmentation and status of core
forests,
(iii) visualizing current forests status and predicting forest
cover for 2026.
II. STUDY AREA
Uttara Kannada district forest region also referred as Canara
circle. Canara Circle comprises of 5 Territorial Forest
Divisions and a wild life division (Figure 1). The divisions
are Haliyal forest includes taluks of Haliyal, part of Supa
taluk; Honnavar forest division comprises taluks of part of
Ankola, Kumta, Honnavar, Bhatkal; Karwar division covers
part of Ankola, Karwar, part of Supa taluks; Sirsi division
covers Sirsi, Siddapura taluks; Yellapur division covers
Yellapur, Mundgod taluks. Dandeli wild life division
(ADTR-Anshi Dandeli tiger reserve) covers major portion of
Supa taluk. The total forest area of Uttara Kannada district is
8296 km2, (as per the legal status) including areas released for
various non-forestry activities and recent orders of
regularization of encroachment. The forest area under the
control of the Forest Department is 7759 km2 (93.53% of the
total forest area). The forest area under revenue and other
departments is 536 km2. These divisions harbors varieties of
endemic flora and fauna. The average rainfall in the region
varies from 4000-5000 mm. The major vegetation types of
Uttara Kannada have been broadly grouped as ‘natural
vegetation’ which includes evergreen, moist deciduous and
dry deciduous forests, ‘plantations or monocultures’ which
includes plantations of Tectona grandis (Teak), Eucalyptus
sp. (Blue gum) Casuarina equisetifolia, Acacia
auriculiformis, Acacia nilotica, and other exotics. The total
population of the district is 14, 37,169 with population
density of 140 persons/km2. The forests are suffering from
many developmental activities and policy interventions,
subsequently leading to heavy removal of lofty trees across
the district.
Figure 1: Study area
III. METHOD
Figure 2 outlines the method adopted for the analysis. The
temporal remote sensing data is collected from global land
cover facility (GLCF) and purchased from national remote
sensing center (http://nrscgov.in), Hyderabad. The field data is
collected using handheld GPS (Global Positioning System
Garmin GPS) across various land uses and forest types. The
supervised classification scheme of Gaussian maximum
likelihood classifier (GMLC) scheme is adopted for land use
analysis under 7 different land use categories as shown in
Table 2 using GRASS GIS (Geographical Analysis Support
System). GRASS is a free and open source geospatial software
with the robust functionalities for processing vector and raster
data available at (http://wgbis.ces.iisc.ernet.in/grass/). The
training data (60%) collected has been used for classification,
while the balance is used for accuracy assessment to validate
the classification. The test samples are then used to create
error matrix (also referred as confusion matrix) kappa (κ)
statistics and overall (producer's and user's) accuracies to
assess the classification accuracies [16]. Forest fragmentation
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category at pixel level is computed through Pf and Pff matrices
using a moving window of 5 X 5 pixels (Figure 3) to maintain
a fair representation of the pixels proportion (Pf) and to
maintain core forest at an appropriate level as the
fragmentation model are scale dependent and threshold
dependent [17, 18]. Details of levels of fragmentation with
discriminant criteria of the each component are listed as core
forest, perforated forest, edge forest, patch forest, transitional
types. The Pf is the proportion of forest pixels among non-
water pixels within a moving window of specified size, and Pff
is the ratio of the number of pixel pairs in cardinal directions
that are both forest divided by the number of pixel pairs in
cardinal directions where either one or both are forested. Pff
estimates the conditional probability that given a pixel of
forest, its neighbor is also forest. The water bodies or river
coarse are considered as non-fragmenting features, because
they act as natural corridors in forested landscape. Non-
forested areas including buildings, roads, agricultural field,
and barren land, along with developed land are considered as
fragmenting features.
Figure 2: Method adopted
The land use analysis has provided spatial pattern and
Markovian process is used to generate transition probability
map and area matrix, obtained based on probability
distribution of the current cell state that is assumed to only
depend on current state (Equations 1 & 2). The neighborhood
influence area is consequently added through rules by various
demands of the land use categories, population growth etc. in
computation of transitional potential and its interaction The
original transition probability matrix (denoted by P) of land
use type should be obtained from two former land use maps.
CA was used to obtain a spatial context and distribution map
based on Markov transitional probability and area by
combining multi criteria land allocation to predict land cover
change over time. The diamond filter of a kernel size of 5*5
pixels was used to create spatially explicit contiguous
weighing factors to measure neighborhood effect or influence.
The pixels nearby of same land use neighborhood have greater
influence than that are far from the existing land use class. The
CA coupled with Markov chain land use predictions of 2013
was made by using the transitional probability area matrix
generated for 2004-2013. The validity of the predictions was
made with the reference land use maps of 2013 (actual) by
evaluating accuracy through the calculation of Kappa index
for location and quantity. Based on these validations then
visualization was made for 2022 by considering equal time
interval.
(1)
where, P(N) is state probability of any times, and P(N1) is
preliminary state probability.
Transition area matrix can be obtained by,
(2)
where, Pij is the sum of areas from the ith land use category to
the jth category during the years from start point to target
simulation periods; and n is the number of land use types. The
transition area matrix must meet the following conditions
i. 0 Pij 1
ii.
Figure 3: Computation of Pf and Pff
S.NO.
Land use
categories
Description
1.
Forest
Evergreen to semi evergreen, Moist deciduous
forest, Dry deciduous forest, Scrub/grass lands
2.
Plantations
Acacia/ Eucalyptus/ hardwood plantations, Teak/
Bamboo/ softwood plantations
3.
Horticulture
Coconut/ Areca nut / Cashew nut plantations
4.
Crop land
Agriculture fields, permanent sown areas
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5.
Built-up
6.
Open fields
7.
Water
Table 1. Land use categories.
IV. RESULTS
The forests of Uttara Kannda are acting as rich resource
base in the Western Ghats and providing livelihood for various
people. The land use analysis depicts the spatio temporal
changes in this biodiversity rich region from year 2004 to
2013 (Figure 4 to 9 and Table 2). The evergreen forest cover
was lost due to infrastructure related projects. The ADTR
region (Figure 4) land use shows slower trend of forest loss
from 89.93% to 87.23 due to protection and inaccessibility.
The fragmentation analysis shows out of 98830.16 ha forest
cover core forest is only 73793.89 ha. The forest cover across
all the other divisions are expressing greater loss of forests
from year 2004. Haliyal division has lost major cover from
2004 (55.48%) to 2013 (45.29%) due to increase in population
pressure and conversion of forest area into monoculture
plantations. The core forest cover (Figure 5) remained is only
27201.14 ha (out of 67392.45 ha) due to monoculture
plantations and intensive agriculture activities led to loss of
core area and resulted in greater edge forests and perforations.
The dense core forests were lost due to construction of series
of dams on river Kali and some area was replaced for
plantations. The core forest remained, which is mostly
concentrated Supa taluk part and Kali river valley regions.
Roads are another driver aid in the loss of core forest cover in
all divisions. The Kaiga power grid lines have bisected thick
tracts of forests and formed additional edges.
The intensifying plantation and regularizing earlier
encroached lands made disappearing of core forest cover from.
The plantations of Acacia auriculiformis; Tectona grandis in
moist deciduous and dry deciduous tracks of Mundgod and
Kirwatti have registered high survival rate as compared to
other ranges led the forest department to replace the natural
regions like grassy blanks and open patches, irrespective of
rainfall, depth of the soil by plantations in Yellapura division
(Figure 6). Karwar is the district head quarter of Uttara
Kannada having greater population pressure on forest for
resources. The execution of series of dams, power plants,
novel base projects directed to diminishing of core forest
cover in this division (Figure 7). Honnavar forest division
(Figure 8) land use analysis shows increase of horticulture and
agriculture activities has led to loss of forest cover. Even
though heavy human pressure exists, the region is still having
rich biodiversity due to major rivers Gangavalli, Agnanashini,
Sharavati and innumerable streams which flows from Ghats to
Arabian Sea. The region has 53.83% area under core forests,
the intensification of commercial plantations and exotic
plantations headed to loss of forests. The more perforated
patches represent small disturbances inside core forests
leading to loss of connectivity. The land conversion is major
problem noticed i.e. conversion of forest to agriculture;
agriculture to coco/areca nut plantations. The rehabilitations
due to river valley projects has altered core forests by creation
of more edges and perforations. The Sirsi division (Figure 9)
comprises evergreen to semi evergreen, moist deciduous, dry
deciduous forests cover type. The market based agriculture
practice encouraged local people to convert the land use for
commercial plantations, followed by developments reached
non-forest cover by 52.12% in 2013.
Figure 4 (a, b, c d): Land use and fragmentation analysis of
ADTR.
Figure 5 (a, b, c, d): Land use and fragmentation analysis of
Haliyal division
Figure 6 (a, b, c, d): Land use and fragmentation analysis of
Yellapura division.
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FOREST
DIVISION
Year
Forest
Plantations
Horticulture
Crop land
Built-up
Open fields
Water
Overall
accuracy
ADTR
2004
101957.1
6549.7
325.73
1981.89
295.61
1824.89
444.86
2004
87.38%
2013
91.24
2013
98,830.16
8,318.31
512.04
2288.41
499.06
2399.42
452.7
HALIYAL
2004
82510.61
24616.93
865.01
27287.71
2377.96
4514.96
6556.37
2013
67392.45
34701.33
784.57
28391.69
4843.13
6114.85
6565.39
HONNAVAR
2004
114650.4
9794.68
16481.16
24059.89
9202.98
9109.46
6670.99
Kappa
2013
102192.3
14261.56
20473.33
24833.27
9587.41
11585.27
7000.69
KARWAR
2004
120770.2
8766.98
3948.27
11833.35
5790.83
5614.8
11104.61
2004
0.81
2013
0.85
2013
115546.4
10155.37
5170.57
11605.47
7860.37
7106.65
10369.37
SIRSI
2004
122730.6
31686.07
16124.67
40588.56
4475.55
3099.85
478.77
2013
107352.4
35798.37
18065.61
44062.57
9889.88
3381.31
679.87
YELLAPURA
2004
82676.2
66193.97
2218.97
28395.48
4079.57
4353.63
2037.44
2013
70850.2
67618.81
1810.37
31784.36
9587.32
6016.1
2147.64
Table 2: Land use analysis from 2004-2013.
Figure 7 (a, b, c, d): Land use and fragmentation analysis of
Karwar division.
Figure 8 (a, b, c, d): Land use and fragmentation analysis of
Honnavar division.
Figure 9 (a, b, c, d): Land use and fragmentation analysis of
Sirsi division.
The CA-Markov analysis is carried out based on the
transitions of land use from 2004-2013. The Markov chain
process estimated transitional rates of each land use based on
2004 and 2013 by loop time of 9 years. With the knowledge of
2004-2013, land use in 2013 and 2022 is predicted under
different conditions (i.e. transition rules, iteration numbers).
This prediction has been done considering water bodies as a
constraint and assumed to remain constant over all time
frames. The model was analyzed for allowable error of 0.15
by validating the predicted versus the actual for the years 2013
land use maps. The validation results showed in Table 3 across
the divisions provides a very good agreement between the
actual and predicted maps of 2013. The accuracy of agreement
between actual land use and predicted land use were shown in
Table 4 with kappa values. The simulated land use (Table 5,
Figure10 to 15) shows likely increase in built-up area and loss
in forest cover across all divisions.
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Division
ADTR
HALIYAL
HONNAVAR
KARWAR
SIRSI
YELLAPURA
year
Simulated
2013
Projected
2022
S-2013
P-2022
S-2013
P-2022
S-2013
P-2022
S-2013
P-2022
S-2013
P-2022
Forest
100361.33
97500.46
82510.61
61,083.26
96556.9
23394.63
121898.88
108,475.11
103273.34
60,165.14
65420.03
51,706.88
Plantations
8189.88
8630.06
24616.93
21,135.98
15434.52
28849.9
13731.73
13,558.41
32842.27
49,974.33
85996.26
73,578.18
Horticulture
342.32
12.9
865.01
1020.84
31327.42
30486.85
8026.31
4196.71
29570.98
30,072.65
7637.09
3582.23
Crop land
2100.57
2703.42
27287.71
32,781.57
22794.66
14160.09
9473.16
17,383.49
42527.26
50,515.87
23315.18
31,127.49
Built-up
192.72
1216.17
2377.96
17,843.95
7054.07
11950.95
5913.65
17,473.49
6686.05
23,329.25
6804.82
26,057.38
Open fields
2173.25
2517.29
4514.96
8281.97
10827.75
6671.26
7120.63
6380.97
3284.99
4225.93
4801.28
8136.24
Water
517.18
432.5
6556.37
6577.65
7313.61
115513.68
11118.13
9821.34
790.34
673.38
2448.57
2156.21
Table 5: Simulated and Predicted land use 2013 & 2022
The process of urbanization is observed to be high in the areas
near project Sea bird, Kaiga power house and the
national/state highways. The analysis highlighted the decline
of forest cover greater in Sirsi division as from 47.16% (2013)
to 27.48% (2022) with increase in monoculture plantations
and built up area. The Honnavar division has witnessed
changes within and in the neighborhood due to the
introduction of major developmental projects that has led to
rapid land conversion. The built-up area shows a greater
increase from 3.69 to 6.25 % and in Karwar division from
3.34 to 9.86% by 2022. The ad-hoc approaches in the
implementation of developmental activities have led to
landslides, higher erosion of top soil, etc. CAMarkov model
has provided insights in terms of change quantification and
constructed as a linear presumption of the Markov model. The
multi-agent models with human’s perception, other
biophysical drivers will increases the precision of prediction
of current study.
Given
Probability of changing to
F
P
H
C
B
O F
W
Forest
0.88
0.02
0.02
0.06
0.02
0.00
0.00
Plantations
0.08
0.84
0.02
0.04
0.02
0.00
0.00
Horticulture
0.05
0.02
0.84
0.05
0.04
0.00
0.00
Crop
0.04
0.01
0.02
0.85
0.08
0.00
0.00
Built-up
0.04
0.02
0.03
0.02
0.89
0.00
0.00
Open fields
0.02
0.02
0.02
0.02
0.02
0.90
0.02
Water
0.02
0.02
0.02
0.02
0.02
0.02
0.90
Table 3: Transition probability matrix for 2004-2013 of
ADTR region
Index
Kno
Klocation
Kstandard
ADTR
0.92
0.93
0.89
HALIYAL
0.9
0.92
0.89
HONNAVAR
0.88
0.87
0.83
KARWAR
0.91
0.87
0.84
SIRSI
0.88
0.86
0.83
YELLAPURA
0.85
0.83
0.82
Table 4: Validation of simulation
Figure 11: Simulated and predicted land use of Haliyal division
Figure 10: Simulated and predicted land use analysis of ADTR
Figure 12: Simulated and predicted land use of Honnavar division
Figure 12: Simulated and predicted land use of Karwar division
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Figure 14: Simulated and predicted land use of Sirsi division
Figure 15: Simulated and predicted land use analysis of
Yellapura division.
CONCLUSION
Land use dynamics across various forest jurisdictions
shows a likely expansion of the non-forest activates that might
occur in forest divisions with a changing socio economic
situation. The Sirsi division has lost major forest cover from
122,730.61 ha (2004) to 107,352.37 ha (2013) due to land use
conversion for non-forest activities. The ADTR shows slower
deforestation rate due to higher protection and Yellapura
division has major loss of core forest cover by plantations and
resulted in greater edges. The division wise assessment depicts
Mundgod and Haliyal are on the edge of devastation. The 85%
of whole landscapes of the divisions are becoming non-forest
usage by 2022. The cultivation in the nearby plots are also
badly affecting natural cover because of diverting stream and
channelizing water in to the field causing dryness of the
forests and its neighborhood. The planning by consideration of
the spatio temporal patterns of forest transitions while
planning for biodiversity conservation, etc. aid in the
appropriate land use decisions and formulation of
ecologically sustainable forest management at landscape level.
REFERENCES
[1] T.V. Ramachandra and B.V. Shruthi, “Spatial mapping of renewable
energy potential,” Renewable and Sustainable Energy Reviews,
11(7):1460-1480, 2007.
[2] Buskirk, Steven, William H. Romme, Federick W. Smith, and Richard
L. Knight, “An Overview of Forest Fragmentation in the Southern
Rocky Mountains” pp3-14, 2000, in Forest Fragmentation in the
Southern Rocky Mountains ed. Richard L. Knight, Frederick W. Smith,
Steven W. Buskirk, William H. Romme, and William L. Baker. Boulder,
Co: University Press of Colorado.
[3] W.F. Laurance, T.E. Lovejoy, H.L. Vasconcelos, E.M. Bruna, R.K
Didham, P. C. Stouffer,C. Gascon, R.O ,Bierregaard, S.G. Laurance, E.
Sampaio, Ecosystem decay of Amazonian forest fragments: a 22-year
investigation,” Conservation Biology, 16, pp. 605–618, 2002.
[4] K.A. Harper, S.E. MacDonald, P.J. Burton, K.D. Chen, J. Brosofske,
S.C. Saunders, E.S. Euskirchen, D. M.S. Roberts Jaiteh, P.A. Esseen,
“Edge influence on forest structure and composition in fragmented
landscapes,” Conservation Biology, 19, 768–782, 2005.
[5] L. Cagnolo, M. Cabido, G. Valladares, “Plant species richness in the
Chaco Serrano woodland from central Argentina: ecological traits and
habitat fragmentation effects,” Biological Conservation, 132, 510–519,
2006.
[6] R.E. Kennedy, P.A. Townsend, J.E. Gross, W.B. Cohen, P. Bolstad,
Y.Q. Wang, P., Adams, “Remote sensing change detection tools for
natural resource managers: understanding concepts and tradeoffs in the
design of landscape monitoring projects,” Remote Sensing of
Environment, 113, 13821396, 2009.
[7] T. V. Ramachandra, Setturu Bharath and Aithal Bharath, “Spatio-
temporal dynamics along the terrain gradient of diverse landscape,”
Journal of Environmental Engineering and Landscape Management,
22:1, 50-63, 2014, doi:http://dx.doi.org/10.3846/16486897.2013.808639.
[8] M. Bürgi, A.M. Hersperger, N. Schneeberger, “Driving forces of
landscape change: current and new directions,” Landscape Ecology,
19(8), 857-868, 2004.
[9] M. Batty, P.M. Torrens, “Modelling and prediction in a complex world,”
Futures, 37 (7), 745-766, 2005.
[10] Bharath Setturu, K.S. Rajan, T.V. Ramchandra, “Status and future
transition of rapid urbanizing landscape in Central Western Ghats CA
based approach” ISPRS Technical Commission VIII Symposium, 09-12
December 2014.
[11] P.H. Verburg, M. Dijst, P. Schot, A. Veldkamp, “Land Use Change
Modelling: Current Practice and Research Priorities,” Geojournal, 61,
309-24, 2004.
[12] G. Caruso, M. Rounsevell, G. Cojocaru, “Exploring a spatiodynamic
neighbourhoodbased model of residential behaviour in the Brussels
periurban area,” International Journal of Geographical Information
Science, 19 (2), 103-123, 2005.
[13] S.J. Walsh, J.P. Messina, C.F. Mena, G.P. Malanson, P.H. Page,
“Complexity theory, spatial simulation models, and land use dynamics
in the Northern Ecuadorian Amazon,” GeoForum 39(2), 867-878, 2008.
[14] R.G. Pontius Jr., D. Huffaker, K. Denman, “Useful techniques of
validation for spatially explicit land-change models,” Ecological
Modelling 179 (4), 445461, 2004.
[15] P.H. Verburg, K. Overmars, “Combining top-down and bottom-up
dynamics in land use modelling: exploring the future of abandoned
farmlands in Europe with the Dyna-CLUE model. Landscape Ecology,”
24, 1167-1181, 2009.
[16] T. Lillesand, R.W. Kiefer & J.W. Chipman, “Remote Sensing and Image
Interpretation,” Fifth Edition, International Edition, John Wiley & Sons,
New York, 2004.
[17] K. Riitters, J. Wickham, R. O'Neill, B. Jones, E. Smith, “Global-scale
patterns of forest fragmentation,” Conservation Ecology 4(2): 3, 2000.
[18] J. Sun, J. Southworth, “Remote Sensing-Based Fractal Analysis and
Scale Dependence Associated with Forest Fragmentation in an Amazon
TriNational Frontier.” Remote Sens., 5, 454-472, 2013.
AUTHORS PROFILE
Bharath Setturu is a Ph.D. student of IIIT-H, and his research interests
include landscape ecology, spatial pattern analysis, landscape modeling, open
source GIS. Published 12 journal papers and 15 conference papers.
Rajan K S obtained his Ph.D. from University of Tokyo. He is head, Lab for
Spatial Informatics (LSI), International Institute of Information Technology,
Hyderabad. Areas of expertise includes Agent based Spatial Modelling
(AGENT-LUC), GIS and Remote Sensing, Land Use and Land Cover .
Ramachandra T V obtained his Ph.D. from Indian Institute of Science (IISc),
Bangalore in Energy and Environment. He has published 184 research papers
in national and international journals, and has more than 118 papers in
conferences and has written 14 books on related topics.
This research hasn't been cited in any other publications.
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