Landscape Dynamics in Relation to Slope and Elevation in Garo Hills of Meghalaya, India using Geospatial Technology

Table of contents

1. Introduction

andscape dynamic is concerned with the effect of spatial heterogeneity on ecological process. The physical environment including climate, geology, topography, plant succession, species extinction and evolution is often regarded as one of the most important factors controlling this heterogeneity of the landscape in mountain areas. Disturbances like shifting cultivation, landslide, floods, deforestation, urbanization, forest fire, and the ecosystem modification are responsible for landscape dynamics (Zimmermann & Eggenberg, 1990). Land use/ coverstudy shows present as well as past conditions of the earth surface and it is a central component and strategy for managing natural resources and monitoring environmental changes (Yadavet al., 2012a). Landscape ecology is the study of patterns and structures across temporal and spatial scales. Spatial patterns observed in landscape result from complex interactions between biotic and abiotic processes and disturbances that occur within environment (Turner et al., 2001). As changes occur in the landscape, the overall structure and composition of ecological community is affected, hence the importance of the study related to landscape is increasing for maintainingthe ecological diversity. Among different environmental factors that produce landscape patches slope and elevation are important parameters that provide varieties of topographical features (Sarma and Barik, 2010). The study of the slope is important not only it provides the variety of topographical features but also provides evidence for the interpretation of complex form of the existing landscape and reflects the evolutionary history of the landform (Fairbridge, 1968). Elevation pattern of landscape have been responsible for many factors like climate, isolation, species-area effects, historic events and biomass productivity of landscape patches (ICIMOD, 2000 andAcharyaet al., 2011).Vegetation is one of the major factors controlling soil erosion, while most soil erosion occurrences are due to removal of vegetation and topsoil (Bochet and Fayos, 2004 and Yadavet al., 2012b). The shifting cultivation accounts for 60 percent global forest loss each year (Leleet al., 2008) and in northeast India annual forest loss is about 10,000 sq.kmdue to this unhealthy practice. The total area affected by shifting cultivation (locally known as jhum) in northeast is estimated to be 44,000 sq.km (Singh, 1990). The jhum cycle in northeast has been decreased from 20 to 30 years in the past to about 5 years (Toky and Ramakrishnan, 1981)and in many areas even up to 3-5 years (Sarma, 2010a). Vegetation and land characteristics of Garo hills of Meghalaya, northeast India are heavily influenced by jhum activities (Figure 1 Remote sensing and geographical information system (GIS) coupled with computer programs allow to use landscape ecological principle for biodiversity characterization more efficiently (Yadavet al., 2013). This technology has improved the efficiency of land use/ cover mapping and change detection with respect to slope and elevation pattern at landscape level. Digital Elevation Model (DEM) is a potential tool for terrain analysis at the varied spatial and temporal scales. The objectives of the present study include generation of slope and elevation maps of Garo hills districts of Meghalaya, preparation of land use/ cover maps for two different decades and to examine the dynamic relationships of slope and elevation with land use/ cover using temporal remote sensing data.

2. Study Area

The Garo Hills of Meghalaya consist of three districts viz., East Garo Hills, West Garo Hills and South Garo Hills (Figure 2). The districts are bordered in the north and west by Assam state, south by Bangladesh and east by West Khasi Hills district of the state. The districts are highly dissected with irregular terrain. The highest point of Garo hills is theNokrek peak with an altitude of 1,412m above msl. The total area of Garo Hills districts is 8,167 sq. km, which is 36.4 percent of the total area of the state (Sarma, 2010b). The soil of the districts is red loam and is poor in silica but rich in clay forming materials. The soil is generally loamy but often found clay to sandy loam. The surface horizon which is about 30 cm thick has colours ranging from reddish brown to dark reddish brown. The soils are rich in organic matter and nitrogen but deficient in phosphorous and potassium and they are acidic in reaction (Sarma and Barik 2012)

3. Materials and Methods

For landscape dynamic study temporal remote sensing imagery of 2001 and 2010 were utilized while for generating digital elevation model 2001 base year was considered (Table 1). The satellite images with bands (7) were stacked to prepare an FCC of bands 3(Red), 2(Green) and 1(Blue). The relevant topographic maps and image were geometrically rectified in 1:50,000 scale using geographic projection system UTM; speroid and datum used were WGS 84 with UTM zone 45N. The GIS and image processing software used are ArcGIS 10, Erdas Imagine 2011 and Quantum GIS 1.6. The paradigm for the study is described in Figure 3. Field verification was carried out during 1 st February to 11 th April 2012. Accuracy assessment of the classification schema is given in Table 2.

4. Results

Four land use/cover classes viz., dense forest (more than 40% canopy cover), open forest (10% to 40% canopy cover),non-forest (less than 10%) and current jhumhave been delineated for the study area (FSI, 2005). For slope three categories of high (above 14 degree), moderate (6 to 14 degree) and low (below 6 degree) are considered. Accordingly for elevation high (above 900 m), moderate (300 to 900 m) and low (below 300 m) categories are fixed.

It is found that in both the years the area under open forest (6,365 sq.km and 4,307 sq.km) has the maximum coverage which is followed by non-forest area (2,155 sq.km and 2,846 sq.km). There is a decrease of 2,058 sq.km open forest during the period while areas under non-forest increased by 1,591 sq.km. The area of dense forest increased in the decade(218 sq.km). This may be due to the efforts put by government and other organizations who are working for the regeneration of the natural forests of Garo hills. This increase is found mostly in the areas under moderate and high slope areas. Loss of open forest areas is found in all the slope categories where maximum loss found in low slope category. Similar trend is followed by non-forest areas. The high slope areas are also utilized for shifting cultivation which is vulnerable in terms of sheet erosion. In fact the areas under shifting cultivation in the high slope areas increased during the decade in considerable proportion (

5. Discussions

Based on Landsat TM (2001) and Landsat ETM+ (2010) data fourbroad types of land use/ cover were observed for the two different years in Garo hills. Classifications of these satellite imagery show that dense forest is confined mostly to the inaccessible area whereas other three types fall mainly in the moderate and low slope and elevation. The primary forest of the districts have been destroyed to a great extent by age old tradition of shifting agriculture which is extensively practiced in the hilly regions of the northeast India (Ramakrishnan, 1992;Yadavet al., 2012). This activity has led to the development of a variety of successional plant communities ranging from open forest to recently abandoned shifting cultivation fields (Prabhu, 2004). In the present study, the proportion of open forestand nonforests increased with the decrease in slope. These areas represent a mosaic of degraded landscape owing to the gentle slope of the area. This finding is similar to that of Susana & Mario (2000) who reported that deforestation may be widespread in areas where slopes are relatively gentle. There is general trend for mountain ecology that with increasing altitude there exists good ecological conditions (Hamilton et al. 1999). This criterion is fulfilling in the present study. The findings of the present research reflect the similar results of Ramesh et al. (1997) who stressed that deforestation process characterized by removal of the smallest and most accessible forest patches, followed by other developmental and livelihood activities. The present study is supported by Sarma and Barik (2010) who revealed that even vulnerable slopes are not spared from shifting cultivation consequences of which could be devastating. Semwalet al. (2004) revealed that deforestation may be widespread in an area where slope is relatively mild in nature. Balaguruet al. (2003) established while relating vegetation with slope angles of Shervayan hills of Eastern Ghats that number of species increases with increasing degree of slopes. Their finding is very much supportive to the present research. Whereas, Smith et al. (2005) while studying relationships between geomorphology and tree density revealed all type of trees in all slope categories but density was high in the stable landforms despite slope variations.

6. VI.

7. Conclusions

Garo hills districts support animpressive forest cover which is mainly concentrated in inaccessible areas and theseshould be conserved for biodiversity. It was observed in this study that the remote forest areas are also slowly encroached by the local people for shifting cultivation, mining and other activities. The districts have witnessed the conversion of forests to other non-forest areas during the last decade. This alteration needs to be checked immediately. After shifting cultivation the fallowland should be allowed to regenerate at least 15-20 years before another cycle. The short cycle not only effects soil fertility but also exposes the top soil for erosion. Further, the conversion of forest areas into other land use should be be undertaken to prevent the area from further deterioration is to educate the people and make them aware of the consequences of the effect of deforestation, mining and shifting cultivation.Landscape dynamics study is important to understand and assess the changes in natural resources due to various natural and anthropogenic reasons. The findings of the present study could be useful for management authority for making strategies for management of natural resources and monitoring its changes in due course of time. Temporal remote sensing data with detailed field observation could be an authentic tool for studying the landscape dynamics in any part of the globe which are environmentally fragile.

8. Global Journal of Human Social Science

Figure 1.
) which have greatly amplified in recent decades with human population, resulting in severely fragmenting previously undamaged forest tracts(Singh et al., 2011).
Figure 2. Figure 1 :
1Figure 1 : Photograph showsthe base in different slopes for cultivation after removing the vegetation by slushing and burning in Nokrek biosphere reserve of Garo Hills
Figure 3. Figure 2 :
2Figure 2 : Location of the study area
Figure 4. Figure 3 :
3Figure 3 : Paradigm for assessing the landscape dynamics in relation to slope and elevation IV.
Figure 5. Figure 6 :
6Figure 6 : Change matrices during 2001 and 2010 in terms of slope (A) and elevation (B) categories
Figure 6. Volume
Figure 7.
Figure 8.
Figure 9.
Figure 10.
Figure 11. Table 1 :
1
study
Path & Row Data Type Date Production
138& 42 Landsat TM 15-12-2001
137& 42 Landsat TM 21-11-2001
137& 43 Landsat TM 26-12-2001
138 &42 Landsat ETM+ 06-02-2010
137 &42 Landsat ETM+ 30-01-2010
137 &42 Landsat ETM+ 30-01-2010
137 &42 LiDAR STRM 2001
(DEMs)
Figure 12. Table 2 :
2
classification
Year Overall classification accuracy Overall kappa statistics D D D D )
2001 85.94% 0.77 (
2010 92.19% 0.85
Figure 13. Table 3 )
3
Data acquirement Collection of field data
DEMs data (LIDAR) Landsat TM for 2001and ETM+ for 2010/ geo-referenced Collection of GCPs of different Lu/Lc classes Identification of drivers of deforestation
Layer stacking
Subset of the Current jhum in
Subset of the study study Area different slope and
Area elevation
Supervised
classification
Slope and elevation Land use/ cover map for 2001 and 2010 Field verifications of different Lu/Lc classes
Change matrix with respect to
slope and elevation Results
during 2001 and 2010
Note: Bcategories.
Figure 14. Table 3 :
3
Land Slope Year 2001 Slope Year 2010
use/cover class Area in km 2 in low Area in km 2 in moderate Area in km 2 in high Total Area in km 2 in low Area in km 2 in moderate Area in km 2 in high Total
Dense forest 35 162 178 375 18 247 328 593
Open forest 1,697 3,403 1,265 6,365 897 2,576 834 4,307
Current jhum 67 86 19 172 107 268 46 421
Non-forest 913 306 36 1,255 1,690 866 290 2,846
Total 2,712 3,957 1,498 8,167 2,712 3,957 1,498 8,167
Figure 15. Table 4 :
4
Land Elevation Year 2001 Elevation Year 2010
use/cover class Area in km 2 in low Area in km 2 in moderate Area in km 2 in high Total Area in km 2 in low Area in km 2 in moderate Area in km 2 in high Total
Dense Forest 88 163 124 375 98 365 130 593
Open Forest 4,146 2,197 22 6,365 2,534 1,762 11 4,307
Current Jhum 107 48 17 172 174 209 38 421
Non-Forest 1,040 173 42 1,255 2,575 245 26 2,846
Total 5,381 2,581 205 8,167 5,381 2,581 205 8,167
Figure 16. Table 5 :
5
Area in Area Area in Area in Area in Area
km 2 in % km 2 % km 2 in %
Dense to open forest 43 1.59 34 0.86 43 2.87 120
Open forest to dense forest 22 0.81 124 3.13 139 9.28 285
Open forest to current jhum 90 3.32 208 5.26 65 4.34 363
Open forest to non-forest 902 33.26 746 18.85 195 13.02 1,843
Current jhum to open forest 14 0.52 152 3.84 13 0.87 179
Non-forest to open forest 67 2.475 217 5.48 22 1.47 306
No changes 1,519 56.01 2,400 60.65 1,003 66.96 4,922
Others 55 2.03 76 1.92 18 1.20 149
Total 2712 100 3,957 100 1,498 100 8,167
Figure 17. Table 6 :
6
Area in Area Area in Area Area in Area
km 2 in % km 2 in % km 2 in %
Dense to open forest 14 0.26 97 3.76 9 4.39 120
Open forest to dense forest 87 1.62 183 7.09 15 7.32 285
Open forest to current jhum 154 2.86 191 7.40 18 8.78 363
Open forest to non-forest 1,637 30.42 195 7.56 11 5.37 1,843
Current jhum to open forest 108 2.00 64 2.48 7 3.41 179
Non-forest to open forest 275 5.12 26 1.00 5 2.44 306
No changes 2,996 55.68 1,791 69.39 135 65.85 4,922
Others 110 2.04 34 1.32 5 2.44 149
Total 5,381 100 2,581 100 205 100 8,167
1
2
3

Appendix A

Appendix A.1

Appendix B

  1. Vegetation mapping and slope characteristics in Shervaryan Hills, Eastern Ghats using remote sensing and GIS. B Balaguru , S J Britto , N Nagamurugan , D Natarajan , S Soosairaj , S Ravipaul , D I Arockiasamy . Current Science 2003. 85 (5) p. .
  2. A vegetation based approach to biodiversity gap analysis in the Agastyamalai region. B R Ramesh , S Menon , K S Bawa . India. Ambio 1997. 26 (8) p. .
  3. , B Singh , S J Phukan , B K Sinha , V N Singh , S K Borthakur . Int. J. Conserv. Sci 2011. 2 (1) p. .
  4. Factors controlling vegetation establishment and water erosion on motorway slopes in Valencia. E Bochet , P Garc?´a-Fayos . Spain. Restoration Ecology 2004. 12 p. .
  5. , Ecotropics . 18 p. .
  6. Soil and water conservation in India. G Singh . Proceedings of Symposium on Water Erosion, Settlement and Resource Conservation, (Symposium on Water Erosion, Settlement and Resource ConservationRI. CSWCTR. Dehradun
    ) 1990.
  7. Land policies, land management and land degradation in the. Icimod . Hindu Kush Himalayas. Nepal Study Report.Kathmandu. Nepal 2000.
  8. Distribution pattern of trees along an elevation gradient of Eastern Himalaya. K B Acharya , J N Sanders , L Vijayan , B Chettri . India. ActaOecologica 2011. 37 p. .
  9. Development and Environment: Development of Geoenergy Resources and its Impact on Environment and Man of Northeast India, K Sarma , S K Barik , R K Rai . Hussain, Z. and Barik, S.K. (ed.) 2004. New Delhi: Regency Publications. p. . (Impact of coal mining on the Nokrek Biosphere Reserve of Meghalaya)
  10. Impact of coal mining on vegetation of Nokrek Biosphere Reserve, K Sarma , S K Barik , R K Rai . Singh O.P. (ed.) 2005. New Delhi: Regency Publications. p. . (Mining Environment Problems & Remedies)
  11. Geomorphological risk and conservation imperatives in Nokrek Biosphere Reserve. K Sarma , S K Barik . Meghalaya Using Geoinformatics. NeBIO 2010. 1 (2) p. .
  12. Shifting cultivation: the sole livelihood of the people of Garo Hills. K Sarma . Meghlaya. Ecotone 2010a. 2 (2) p. .
  13. A brief profile of Meghalaya. K Sarma , R Kaul , S K Tiwari , S Kyarong , R Dutta , V Menon . Canopies and Corridors: Conserving the forest of Garo Hills with Elephant and Gibbon as Flagships, (Delhi
    ) 2010b. p. . (Wildlife Trust of India)
  14. Coal mining impact on soil of Nokrek Biosphere Reserve. K Sarma , S K Barik . Meghalaya. Indian Journal of Environmental Protection 2012. 32 (2) p. .
  15. Forêtsetsilviculture en montagne, L S Hamilton , D A Gilmour , D S Cassels . Ives, J.D. and Les, M.D.L.M (ed.) 1999. Grenoble. France. p. .
  16. Landscape ecology in theory and practice, pattern and process, M Turner , R Gardner , R O'neill . 2001. New York: Springer-Valeg.
  17. Assessing forest fragmentation in northeastern region (NER) of India using landscape matrices. N Lele , P K Joshi , S P Agrawal . Ecological Indicators 2008. 8 p. .
  18. , North-East Meghalaya , India . Journal of Biodiversity Management & Forestry 1 (1) p. .
  19. Ecologieet Bioge´ographie alpines: comparison of vegetation and geomorphology: problems and approach. N Zimmermann , S Eggenberg . Rev. Valdo?taine Hist. Nat. Supple´ment 1990. 48 p. .
  20. Land use and deforestation in the highlands of Chiapas, O Susana , G Mario . 2000. Maxico.
  21. Impact of Slash-And-Burn Agriculture on Forest Ecosystem in Garo Hills Landscape of, P K Yadav , M Kapoor , K Sarma . 2012.
  22. Land use land cover mapping, change detection and conflict analysis of Nagzira-Navegaon corridor, Central India using Geospatial technology. P K Yadav , M Kapoor , K Sarma . International Journal of Remote Sensing and GIS 2012b. 1 (2) p. .
  23. The review of biodiversity and conservation study in India using geospatial technology. P K Yadav , K Sarma , S Dookia . International Journal of Remote Sensing and GIS 2013. 2 (1) .
  24. Shifting agriculture and sustainable development: an interdisciplinary study from northeastern India. UNESCO-MAB Series, P S Ramakrishnan . 1992. Paris; Carnforth, Lancs, U.K: Parthenon Publication. 424.
  25. Patterns and ecological implications of agricultural land-use changes: a case study from Central Himalaya. R L Semwal , S Nautiyal , K K Sen , U Rana , R K Maikhuri , K S Rao , K G Saxena . India. Agric. Ecosyst. Environ 2004. 102 p. .
  26. The encyclopedia of geomorphology. Encyclopedia of earth science series, R W Fairbridge . 1968. New York: Reinhold Book Corporation. 3 p. 1295.
  27. Impact of human activities on plant biodiversity of Nokrek biosphere reserve ofMeghalaya, S D Prabhu . 2004. North Eastern Hill University. Shillong. India (Ph.D thesis)
  28. Geomorphology, soil texture and tree density in a seasonal Savanna in eastern Venezuela, S Smith , J F Silva , M R Fariñas . 2005.
Notes
1
© 2013 Global Journals Inc. (US)2 20
2
© 2013 Global Journals Inc. (US)
3
© 2013 Global Journals Inc. (US)properlyplanned. The most important step that needs to Year
Home 
Date: 2013-01-15