Geospatial analysis of forest fragmentation in Uttara Kannada District, India
© T V et al. 2016
Received: 7 January 2015
Accepted: 14 April 2016
Published: 23 April 2016
Landscapes consist of heterogeneous interacting dynamic elements with complex ecological, economic and cultural attributes. These complex interactions help in the sustenance of natural resources through bio-geochemical and hydrological cycling. The ecosystem functions are altered with changes in the landscape structure. Fragmentation of large contiguous forests to small and isolated forest patches either by natural phenomena or anthropogenic activities leads to drastic changes in forest patch sizes, shape, connectivity and internal heterogeneity, which restrict the movement leading to inbreeding among Meta populations with extirpation of species.
Landscape dynamics are assessed through land use analysis by way of remote sensing data acquired at different time periods. Forest fragmentation is assessed at the pixel level through computation of two indicators, i.e., P f (the ratio of pixels that are forested to the total non-water pixels in the window) and P ff (the proportion of all adjacent (cardinal directions only) pixel pairs that include at least one forest pixel, for which both pixels are forested).
Uttara Kannada District has the distinction of having the highest forest cover in Karnataka State, India. This region has been experiencing changes in its forest cover and consequent alterations in functional abilities of its ecosystem. Temporal land use analyses show the trend of deforestation, evident from the reduction of evergreen - semi evergreen forest cover from 57.31 % (1979) to 32.08 % (2013) Forest fragmentation at the landscape level shows a decline of interior forests 64.42 % (1979) to 25.62 % (2013) and transition of non-forest categories such as crop land, plantations and built-up areas, amounting now to 47.29 %. PCA prioritized geophysical and socio variables responsible for changes in the landscape structure at local levels.
Terrestrial forest ecosystems in Uttara Kannada District of Central Western Ghats have been experiencing threats due to deforestation with land use changes and fragmentation of contiguous forests, as is evident from the decline of interior forests and consequent increases in patch, transitional, edge and perforated forests. Interior or intact forest cover in this ecologically fragile region is now 25.62 %. Considering the accelerating rates of forest fragmentation in recent times, the focus should be on reforestation and regeneration of natural vegetation to sustain food and water security and the livelihood of local populations. This requires innovation with holistic approaches in the management of forests by involving all local stakeholders to minimize the encroachment of forests, and improvements in regeneration.
Any landscape is a mosaic of heterogeneous interacting dynamic elements, i.e., manifestations of natural and anthropogenic processes. The structure of a landscape (size, shape and configuration) affects its functional aspects such as bio-geo chemical cycling and hydrologic regimes. The interactions among the landscape elements result in the flow of nutrients, minerals and energy, which contribute to the functioning of the landscape. Forest ecosystems constitute a key component of the global carbon cycle that account for over two-thirds of net primary production on land through photosynthesis converting solar energy into biomass (Roy et al. 2001; MEA 2005; Ramachandra et al. 2013). Forest ecosystems offer timber and non-timber forest products (NTFP), such as medicinal resources, fuel wood and as well provide recreational values (Kindstrand et al. 2008). They aid as biodiversity repositories (Li et al. 2009), restrain soil erosion (Nandy et al. 2011), prevent landslides given that tree roots bind soil, regulate air humidity, temperature and mitigate global warming (Cabral et al. 2010) by absorbing 30 % of fossil fuel CO2 emissions (Pan et al. 2011). The goods and services provided by forested landscapes are vital to the socioeconomic development of human populations (DeFries et al. 2004) and their survival (Ramachandra et al. 2013). At a large-scale land use, more recent land cover changes (LULC) are altering the ecosystem structure, affecting the goods and services of the ecosystem. These disturbances have resulted in fragmentation of forests with a mosaic of natural patches surrounded by other land uses (Ramachandra and Kumar 2011). A host of anthropogenic activities, such as tree logging, conversion of forest land to agriculture, intense agricultural practices, forest fire and unplanned infrastructural development have contributed to the disruption of the contiguity of forests in predominantly natural landscapes (Buskirk et al. 2000; Boogaert et al. 2004). An alteration in forest structure through fragmentation of forests has affected its functional abilities, as is evident from the decline in water yield, carbon sequestration potential and biodiversity. (Diaz et al. 2006; Ramachandra and Kumar 2011).
Quantification of forest fragmentation
Land use (LU) changes driven by anthropogenic activities alter the structure of a landscape, which adversely affect the functional aspects of an ecosystem. Land use patterns are the collective result of interactions among local geophysical indicators, such as elevation, slope and rainfall and agro-climatic indicators, demographic variation, market forces and related development policies (Munroe et al. 2004; Ameztegui et al. 2010). Numerous studies focusing on deforestation at the landscape level have explored the spatial patterns and interactions among the geophysical elements of the landscape and its dynamics (Nelson and Geoghegan 2002; Alix-Garcia et al. 2005; Echeverria et al. 2008; Ramachandra and Kumar 2011; Ramachandra et al. 2013). These changes, measured at temporal scales, help in monitoring ecosystems and aid in the implementation of location specific mitigation measures. Spatial data acquired remotely through space-borne sensors (remote sensing data) at regular intervals help in assessing the temporal changes in spatial patterns (Ramachandra et al. 2014a). Remote sensing data (RS) with geographic information systems (GIS) have made significant contributions to the examination of spatial-temporal patterns and processes of forest ecosystems (Nandy et al. 2011; Ramachandra et al. 2012a) in criteria based decision-making and selection of the optimal alternative. The availability of remote sensing data, with improvements in resolutions (spatial, spectral and temporal resolutions), enables the creation of land use and land cover maps (Chen et al. 2015), as well as that of innovative analytical techniques and helps to monitor changes in cost-effective ways (Bharath et al. 2012a). Changes in forested landscapes of the Western Ghats have been more sudden since the middle of the last century due to the impetus of industrialization policies as a consequence of globalization. Quantification of forest fragmentation in an ecologically fragile region, such as the Western Ghats will help in formulating appropriate mitigation measures towards the conservation of biodiversity.
Understand the prevailing forest cover dynamics from 1979 to 2013 and evaluate the causal factors contributing to forest changes;
Explore the spatial-temporal patterns of forest fragmentation and quantification of the extent of fragmentation;
Evaluate the role of geophysical variables in forest cover changes and assess the effects of such variables on the patterns of forest loss at a temporal scale and
Suggest management options to mitigate forest loss and fragmentation to restore and sustain the ecosystem.
Quantification of spatial-temporal forest changes and extent of fragmentation
Land use analysis and detection of change
Changes in land use during the last four decades were assessed using temporal RS data. This helped in determining the extent and causes of transitions in the landscape. Spatial data analyses involved (i) pre-processing, (ii) vegetation cover and (iii) land use analyses. Pre-processing involved geo-referencing, rectification and cropping of data pertaining to the study region. Geo-referencing was carried out through ground control points collected from the field using pre calibrated GPS and from known points, such as road intersections, collected from geo-referenced topographic maps published by the Survey of India. The Landsat data of 1979, with a spatial resolution of 57.5 m × 57.5 m, was resampled to 30 m comparable to the 1989–2013 data, which are 30 m × 30 m (nominal resolution). The Landsat ETM+ bands of 2013 were corrected for the SLC-off (Scan Line Corrector failed) by using image enhancement techniques and nearest-neighbor interpolation.
Land use analysis involved (i) the generation of False Color Composites (FCC) of remote sensing data (bands–green, red and NIR), which helped in locating heterogeneous patches in the landscape, (ii) selection of training polygons by covering 15 % of the study area (polygons are uniformly distributed over the entire study area), (iii) loading the co-ordinates of these training polygons into GPS, (vi) the collection of corresponding attribute data (land use types) for these polygons from the field, where the GPS helped in locating respective training polygons in the field, (iv) supplementing this information with Google Earth. In the end, 60 % of the training data was used for classification, with the balance going for validation or accuracy assessment (Ramachandra et al. 2012c). The land use analysis was performed using a supervised classification technique based on the Gaussian maximum likelihood (GML) algorithm with training data (collected from the field using GPS). This evaluated quantitatively the variance and covariance of spectral response patterns of land uses based on a GML estimator (Atkinson and Lewis 2000; Ramachandra et al. 2012c). Land use classifications using temporal data was carried out through the open source program GRASS - Geographical Resources Analysis Support System (http://ces.iisc.ernet.in/grass). Accuracy assessments of the classified information have been performed (Lillesand and Keifer 1987; Liu et al. 2007) to evaluate the performance of classifiers through the computation of an error matrix, kappa (κ) statistics and overall accuracies.
Forest fragmentation model implementation
Fragmentation components and their description
Forest pixels are far away from the forest-non forest boundary. Interior forested areas are surrounded by thicker forested areas.
(P f = 1). All pixels surrounding the center pixel are forest.
Forest pixels comprising small forested areas surrounded by non-forested land cover.
(P f < 0.4). A pixel is part of a forest patch on a non-forest background, such as a small wooded lot within a built-up area.
Forest pixels forming the boundary between an interior forest and relatively small clearings (perforations) within the forested landscape.
(P f > 0.6 and P f–P ff > 0). Most pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch. This would occur if small clearings were made within a patch of forest.
Forest pixels that define the boundary between interior forest and large non forested land cover features.
(P f > 0.6 and P f–P ff < 0). Most pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of a forest. This would occur along the boundary of a large built-up area, or agricultural field.
Areas between edge type and non-forest types. If higher pixels are non-forest then they will be tending to non-forest cover with higher degree of edge.
(0.4 < P f < 0.6). About half of the cells in the surrounding area are forested and the center forest pixel may appear to be part of a patch, edge, or perforation depending on the local forest pattern.
Principal Component Analysis (PCA)
Unplanned urbanization, geography, industrialization, government policies, economic reforms and population growth are the major factors driving landscape changes (Gong et al. 2013). Socio-economic and bio-geophysical variables elucidate the role of anthropogenic forces in forest transitions, altering landscape structures and their composition (Ramachandra et al. 2014a). These geophysical variables and socio-economic factors aid as drivers of land use changes in the landscape (Nelson et al. 1999; Barbier 2001; Timar 2011; Cho et al. 2015).
The primary objective of geospatial statistical analysis is to quantify the correlation between socio-economic and bio-geophysical variables with the fragmentation of forests over a set of non-water pixels in a landscape through multiple linear regression (MLR). But, MLR explains the output data as a weighted sum of individual correlations with the assumption that any individual variable/feature in the input set is linearly independent. A high level of interdependence among input variables imply that forest fragmentation correlations do not have an independent biological interpretation. To reduce redundancy in multivariate data, a decomposition of eigen values is used in Principal Component Analysis (PCA). It helps in noise reduction and also in prioritizing variables, responsible for the variation in spatial landscape processes (Morris et al. 2009). Prioritizing these variables based on their role in land use changes will help to evolve management strategies and sustain landscape elements with their interactions (Todd and Kerr 2009).
PCA explores multivariate patterns of all forest patch types versus spatial processes based on correlation matrices and frames clusters and relates spatial heterogeneity among variables correlated with one another and responsive to multiple principal components (PC). These derived components are linear function of the original data set. The components derived are ordered by decreases in variance, i.e., PC1 will have the largest variance among n components and PCn will have the smallest variance. After the extraction of the PCA eigenvalues, Scree plot is generated to prioritize the number of significant principal components (Jackson 1993). Finally, a series of ordinations were created to interpret the PCs visually and determine their fragmentation patterns. The sum of the eigenvalues is equal to the variance of the original data set, which preserves the original variation. The table of factor loadings explains the contribution of each variable to the derived components. A component is oriented towards that variable which has the maximum loading on it.
PCA with geo-physical variables aid in understanding the causal factors of environmental vulnerability. The positively correlated spatial parameters with geophysical and socio-economic variables serve as key descriptors of land use transitions (Salvati et al. 2008). PCA analyzes the variance of variables and reorganizes it into a new set of uncorrelated independent components (principal components) equal to the number of original variables as linear combinations of the measured variables (Swan and Sandilands 1995). These combinations are based on weights (eigenvectors) and the loading for each item/variable is the correlation between components, which serve to demarcate clusters of similar patterns (Colson et al. 2011). PCA aided in prioritizing bio-geophysical and socio variables that act as agents of changes in vegetation cover. The variables used in PCA were normalized (Abdi and Williams 2010; Bell et al. 2015) by computing Z-scores (Normalized (X)).
Temporal land use changes from 1979 to 2013 and accuracy assessment
Moist deciduous forest
Evergreen to semi evergreen forest
Coconut/Areca nut/Cashew nut plantations
Dry deciduous forest
Overall accuracy (%)
Forest cover change from 1979 to 2013
Natural forest cover
Total area (ha)
Temporal changes in forest fragmentation at landscape level from 1979 to 2013
Figure 7b shows a decline in the area of interior forests from 64.42 % (1979) to 53.33 % (1989), with increase in edge forests (12 %). The major activities during this period were industrialization, infrastructure development, intensified agriculture, manganese mining, a ferromanganese plant, a paper mill and plantations. The provision of forest resources to industries at highly subsidized rates and permission to polluting industries in the ecologically sensitive regions have contributed to the decline of forests and contamination of natural resources. Unplanned developmental activities such as a series of large scale power projects, manganese mining, a ferromanganese plant, a paper mill and several irrigation projects have led to the retreat of forests, with their degradation evident in the form of barren hilltops. The mismanagement of Kans (‘sacred forests’ protected by local communities) and reserve forests also aggravated the situation towards the loss of interior, contiguous forests (Chandran 1989). Figure 7c shows the fragmentation status for the year 1999; the region lost a major portion of its interior forest and reached 40.74 % from 53.33 % (1989) with the increase in edge forests to 16.35 %. Drivers of these changes are the implementation of a series of hydroelectric projects, the construction of national routes NH-17, NH-63, NH-204, the Konkan railway line and other infrastructure projects. Figure 7d illustrates the status of forests (in 2013) with 25.62 % interior forests and 17.48 % of edge forests, as well as the loss of connectivity between interior forest patches. These interior forests exist now only in the form of protected areas - sanctuaries, protected areas, sacred groves or Kans. The area under non-forests has increased from 36.07 (1999) to 47.3 % (2013) with an increase of edge and perforated patches.
Component loadings & variance for the year 1979
Cumulative variance (%)
Component variances for the year 1979
Moist deciduous forest
Evergreen to semi evergreen forest
Component loadings for the year 2013
Cumulative variance (%)
Component variances for the year 2013
Moist deciduous forest
Evergreen to semi evergreen forest
Dry deciduous forest
Forest clearing due to anthropogenic activities has been a major ecological problem (Laurance 1999; Etter et al. 2006), affecting biodiversity. Fragmentations of forests have resulted in habitat destruction and changes in the dispersal and migration processes (Armenteras et al. 2003; Etter et al. 2006; Eldegard et al. 2015). Shrinkages in animal habitat have led to inbreeding pressure, resulting in the extirpation of species, highlighting the intimate relationship between species and habitat. Edges contain communities different from interior forests due to an altered climate with higher light availability, loss of soil moisture, increased incursion of predators and competitors. As a consequence of fragmentation, changes in microclimatic near edges have favored the establishment of alien species such as Lantana camara, Chromolaena odorata and other species. This has caused a decline in the of native species, particularly in forests highly fragmented by transmission lines such as in the Haliyal and Mundgod taluks and at the lower slopes of Supa and Sirsi taluks. Forest patches shelter rare endemic species in human dominated landscapes. Studies have reported their role in pollination, maintenance of different life cycle phases of species, diversity and seed dispersal by harboring honey bees, small mammals, avifauna survival and many others (Bodin et al. 2006; Page et al. 2010). The Uttara Kannada District with relic forests (sacred forests/groves) and highly productive landscapes conserve local biodiversity and offer important ecological services, as well as improvement in the livelihood of local communities (Ray and Ramachandra 2010). These intact forests provide shelter for wild fauna and also benefit village communities with an array of forests goods and services such as hydrological functions, fuel wood and timber.
Forest fragmentation analyses provide vital insights to the potential effects of human disturbances (Hobbs and Yates 2003), through spatial descriptors of landscapes besides social and economic factors. The analysis of spatial patterns of forest changes should aid in formulating appropriate management strategies to conserve these threatened ecosystems. Analyses of forest changes are possible due to the availability of temporal remote sensing data. However, the spatial resolution of remote sensing data plays an important role in the analysis of spatial landscape patterns. Kernel sizes and resolution are intimately related in fragmentation analysis. Smaller kernel sizes have the effect of decreasing the average inter patch distance by considering smaller patches in the neighborhood, which also reduces the edge effect. Fragmentation analysis is also affected by up-scaling the resolution of remote sensing data as changes in spatial heterogeneity on a micro scale may not be detected using coarse spatial resolutions (Kitron et al. 2006). Bharath et al. (2012b) has elucidated the dependency of the role of spatial resolution on measuring landscape structures in order to understand the different landscape patterns. The results reveal that landscape metrics based on area or patch cover are sensitive to spatial resolution. Fragmentation indices are also explicit in sensitivity across various spatial resolutions of remote sensing data. Gupta et al. (2000) highlights the problem of upscaling data by comparing correlations among reflectance and areas of land use categories. Their results indicate the loss of land use information of about 40 % in proportion where upscaling was performed.
Kernel sizes and resolution are intimately related in fragmentation analysis. The kernel or scale size can be consistent with respect to the pixel resolution and an increase of kernel size does not change the input data but increases the width of the non-interior classes, at the expense of interior forests, which maintain their overall proportion and the shapes of their features. Ostapowicz et al. (2008) explains the relationship between the resolution and kernel size by assessing and monitoring the structure of landscape patterns from multi-scale land-cover maps. Riitters et al. (2004) demonstrated the appropriateness of 5 × 5 kernel at a nominal resolution of 30 m. The scale effects on different forest composition and configuration were appraised by sensitivity analysis with various combinations of pixel size and kernel size parameters and comparing frequencies of pattern classes in the entire forest area under study.
Reforestation and afforestation practices have been helpful in partially addressing the negative consequences of forest loss, through carbon sequestration, erosion control and non-consumptive use of forest products. However, the introduction of exotic species would impact the native forest patches in the neighborhood (Shigesada and Kawasaki 1997; Ramachandra et al. 2013). The introduction of Acacia auriculiformis, Tectona grandis and other exotic species in the forested regions and grasslands of this district has appalling effects on biodiversity due to habitat destruction, decline in water resources and unavailability of food especially for grazing mammals, which in turn has affected the prey stock of wild carnivores (Rao et al. 2012). Many commercial plantations have come up in the valleys by removing the natural vegetation, even in places with ecologically important ecosystems such as the Myristica swamps (Chandran and Mesta 2001). In this context, the current conservation strategy needs to focus on the local regeneration of natural forests and maintain continuity of forests, which helps in sustaining the livelihood of dependent populations. This requires motivation, conviction and commitment among major stakeholders, i.e., the forest fringe dwellers as well as forest officials. Restoration of forests with native species at watershed levels will help in mitigating the impact of forest fragmentations and improve hydrological services and biodiversity. The existing village grazing lands needs to be demarcated and managed by involving local stakeholders. This would help in mitigating grazing impacts in natural forests and also improves the prospects of forest regeneration. Prohibition of clear felling in the intact primeval forests would aid in preserving the structure of this ecosystem while enhancing its functional aspects. Joint management of forests by involving all stakeholders - local communities and others, would help in curtailing illegal logging, encroachments, wildlife protection and sustainable management of forests.
The Western Ghats, the repository of diverse biological organisms is one among 35 global hotspots of biodiversity. This region has been experiencing large-scale land cover changes with the fragmentation of primeval forests. Current research is attempting to quantify spatial-temporal patterns of land use dynamics and fragmentation of forests using temporal remote sensing data. The analyses would be useful in evolving appropriate forest management strategies to mitigate impacts of adverse land use dynamics. Temporal land use analyses show a decrease in the evergreen forest cover from 57.31 % (1979) to 32.08 % (2013). Forest fragmentation analysis based on remote sensing data of the 1979–2013 period helped in assessing the spatial patterns of forests changes under patch, transitional, edge, perforated and interior cover. The study region, as an ecologically fragile area, is now left with only 25.62 % of interior forests and the spatial extent of non-forests is 47.3 % (2013), which highlights the need to restore forests. The district has 18.5 % of its area under monoculture plantations in the Haliyal and Mundgod taluks. Conservation planning of forest ecosystems needs to be holistic at watershed levels involving all stakeholders. Restoration of forests with native species would enhance hydrological services and biodiversity. By active participation in forest restoration initiatives and micro-level planning, stakeholders of the Western Ghats are likely to gain as promoters and guardians of biodiversity and hydrology. Rendering such service would help in mitigating global climatic change and serve the cause of forest ecosystems in global biodiversity hotspots.
We are grateful for the sustained financial and infrastructure support to ecological research in the Western Ghats to the following institutions: (i) the Ministry of the Environment, Forests and Climate Change, Government of India, (ii) the NRDMS division of the Ministry of Science and Technology (DST), Government of India, (iii) the Karnataka Biodiversity Board, Government of Karnataka, (iv) the Western Ghats Task Force, Government of Karnataka and (v) the Indian Institute of Science.
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