Analysis of spatial patterns of forest fragmentation

Transcription

1 Mapping and Quantitative Assessment of Vegetation of Jiribam Sub-Division, Imphal East Chapter 5 Analysis of spatial patterns of forest fragmentation Introduction Review of Literature Methodology Results and Discussion Conclusion References 99

2 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). 100

3 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, 101

4 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 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 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 102

5 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 ERDAS Imagine, ArcGIS, SPLAM and FRAGSTATS software were used in their study. From 1973 to 1990, more than km 2 of dense forest (rate of deforestation = 3.62) and from 1990 to 2004, km 2 (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 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 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 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. 103

6 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(n F / n NF ) 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 ( ha), Large ( 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). 104

7 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 (m 2 ) 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 (m 2 ) 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 105

8 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. 106

9 Table 11. Spatial accounting of land use/land cover. Class Area-ha % of area No. of patches % of patches Forest Agriculture Built up area Wasteland Water Bodies Other land use Grand Total 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 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. 107

10 Table 12. Landscape metrics for forests of Jiribam Sub-Division. Sl.no. Landscape metrics Value Patch density and size metrics 1 No. of patches Mean Patch Size of forest (ha) Patch density/100 ha Edge metrics 4 Edge Density (m/ha) Mean Patch edge (m) 2743 Shape metrics 6 Perimeter to area ratio Largest Patch Index (%) 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. 108

11 Table 13. Size class distribution of forest fragments. Sl. No. Patch class Area-ha % of area No. of patches % of patches 1 <25 ha ha ha ha >200 ha Grand Total Fig Line chart shows representation of various Forest fragments. 109

12 Fig Fragmentation map of Jiribam Sub-Division. 110

13 Fig 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. 111

14 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. 5.6 References Ambastha, K.R. and Jha, C.S. (2010). Geospatial Analysis of Tamil Nadu Eastern Ghats Forest Types at Landscape level with reference to Fragmentation and Species Diversity, J. Indian Soc. Remote Sens., 38: Armenteras, D.; Gast, F. and Villareal, H. (2003). Andean forest fragmentation and the representativeness of protected natural areas in the eastern Andes, Colombia, Biological Conservation, 113: Behera, M.D. and Roy, P.S. (2010). Assessment and validation of biological richness at landscape level in part of the Himalayas and Indo-Burma hotspots using geospatial modeling approach, J. Indian Soc. Remote Sens., 38:

15 Broadbent, E.N.; Asner, G.P.; Keller, M.; Knapp, D.E.; Oliveira, P.J.C. and Silva, J.N. (2008). Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon, Biological Conservation, 141: De, A. and Tiwari, A.K. (2008). Estimation of Patchiness: A Measure of Fragmentation in the Rajaji-Corbett National Parks and Adjoining Areas, Uttarakhand, India, Int. J. of Ecology and Environmental Sciences, 34(4): Forman, R.T.T. and Godron, M. (1986). Landscape Ecology, John Wiley & Sons, New York Garcia-Gigorro, S. and Saura, S. (2005). Forest Fragmentation Estimated from Remotely Sensed Data: Is Comparison Across Scales Possible? Forest Science, 51(1): Giriraj, A.; Murthy, M. S. R. and Beierkuhnlein, C. (2010). Evaluating forest fragmentation and its tree community composition in the tropical rain forest of Southern Western Ghats (India) from 1973 to 2004, Environmental Monitoring and Assessment, 161:29 44 IIRS. (2002). Biodiversity Characterisation at Landscape Level in North-East India using Satellite Remote Sensing and Geographic Information System, Indian Institute of Remote Sensing, Department of Space, Government of India, Dehradun, Uttarachal, India Jha, C.S.; Goparaju, L.; Tripathi, A.; Gharai, B.; Raghubanshi, A.S. and Singh, J.S. (2005). Forest fragmentation and its impacts on species diversity: An analysis using remote sensing and GIS, Biodiversity and Conservation, 14: Kharuk, V.I.; Im, S.T.; Ranson, K.J. and Naurzbaev, M.M. (2004). Temporal dynamics of larch forest in the forest-tundra ecotone, Doklady Earth Sciences, 398: McGarigal, K. and Cushman, S.A. (2002). Comparative evaluation of experimental approaches to the study of habitat fragmentation effects, Ecological Applications, 12: McGarigal, K. and Marks, B.J. (1994). FRAGSTATS, Spatial pattern analysis program for quantifying landscape structure, Version 2.0, Corvallis: Forest Science Department, Oregon State University 113

16 McGarigal, K.; Cushman, S. A. and Ene, E. (2012). FRAGSTATS v4, Spatial Pattern Analysis Program for Categorical and Continuous Maps, University of Massachusetts, Amherst, fragstats/fragstats.html. Midha, N. and Mathur, P.K. (2010). Assessment of Forest Fragmentation in the Conservation Priority Dudhwa Landscape, India using FRAGSTATS Computed Class Level Metrics, J. Indian Soc. Remote Sens., 38: Munsi, M.; Areendran, G.; Ghosh, A. and Joshi, P.K. (2010). Landscape Characterisation of the Forests of Himalayan Foothills, J. Indian Soc. Remote Sens., 38: Prasad, P.R.C.; Reddy, C.S. and Dutt, C.B.S. (2007). Phytodiversity Assessment of Tropical Rainforest of North Andaman Islands, India, J. of Forestry, 1(1): Rashid, I.; Romshoo, S.A. and Vijayalakshmi, T. (2013). Geospatial modelling approach for identifying disturbance regimes and biodiversity rich areas in North Western Himalayas, India, Biodiversity Conservation, DOI /s Reddy, C.S.; Babar, S.; Sudha, K.; Sudhakar, S. and Raju, V.S. (2008). Vegetation cover mapping and landscape level disturbance gradient analysis in Warangal district, Andhra Pradesh, India using satellite Remote Sensing and GIS, Space Research Journal, 1(1): Reddy, C.S.; Debnath, B.; Krishna, P.H. and Jha, C.S. (2013). Landscape level assessment of critically endangered vegetation of Lakshadweep islands using geo-spatial techniques, J. Earth Syst. Sci., 122(2): Reddy, C.S.; Jha, C.S. and Dadhwal, V.K. (2013). Assessment and Monitoring of Long term Forest cover changes in Odisha, India using Remote sensing and GIS, Environmental Monitoring and Assessment, 185: Reddy, C.S.; Rao, P.R.M.; Pattanaik, C. and Joshi, P.K. (2009). Assessment of large scale deforestation in Nawarangpur district, Orissa, India using remote sensing and GIS, Environmental Monitoring and Assessment, 154: Riitters, K.H.; Wickham, J.; Neill, R.; Jones, B. and Smith, E. (2000). Global-scale patterns of forest fragmentation, Conservation Ecology, 14:

17 Roy, P.S.; Dutt, C.B.S. and Joshi, P.K. (2002). Tropical forest resource assessment and monitoring, Tropical Ecology, 43(1): Roy, P.S. and Joshi, P.K. (2001). Landscape fragmentation & biodiversity conservation, Roy, P.S. and. Tomar, S. (2000). Biodiversity characterization at landscape level using geospatial modeling technique, Biological Conservation, 95: Singh, J.S.; Roy, P.S.; Murthy, M.S.R. and Jha, C.S. (2010). Application of Landscape Ecology and Remote Sensing for Assessment, Monitoring and Conservation of Biodiversity, J. Indian Soc. Remote Sens., 38: Southworth, J.; Nagendra, H. and Tucker, C. (2002). Fragmentation of a Landscape: incorporating landscape metrics into satellite analyses of land-cover change, Landscape Research, 27(3): Srivastava, S.; Singh, T. P.; Singh, H.; Kushwaha, S. P.S. and Roy, P. S. (2002). Assessment of large-scale deforestation in Sonitpur district of Assam, Current science, 82(12): Whitmore, T.C. (1997) Tropical forest disturbance, disappearance, and species loss, in: Laurance, W.F., Bierregaard, R.O. (eds.), Tropical Forest Remnants: Ecology, Management, and Conservation of Fragmented Communities. University of Chicago Press, Chicago, Illinois, 3-12 Wilkie, D.S. and Finn, J.T. (1996). Remote sensing imagery for natural resources monitoring: a guide for first-time users, Columbia University Press, New York 115