, Vol 9(30), DOI: 10.17485/ijst/2016/v9i30/99022, August 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Change Detection of Forest Vegetation using Remote Sensing and GIS Techniques in Kalakkad S. Amar Balaji *, P. Geetha and Soman K. P. Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapeetham Amrita University, Coimbatore - 641112, Tamil Nadu, India; balaji.amar@outlook.com; p_geetha@cb.amrita.edu Abstract Background/Objectives: Advancements in the field of remote sensing techniques and sensors used have made monitoring of forest resources an easier task. Forests are ecosystems that provide habitat and fodder for wild animals, timber and help maintain the global temperature balance. They face threats both by nature and mankind. So therein comes the need to monitor vegetation from time to time, for preserving the ecosystem. Methods/Statistical Analysis: The Kalakkad Mundanthurai Tiger Reserve (KMTR) area is posed to such threats leading to change in forest cover and type. Multi-temporal Landsat imageries were used to study the area. A change detection analysis was carried out to determine the disruptions in forest cover from 2005 to 2015. Findings: Overlaying the classified multi-temporal images indicated significant changes in forest cover. Statistical analysis shows the approximate amount of vegetation affected and afforested over the time period. Applications/Improvements: The findings can be included for the vegetation monitoring and conservation activities carried out by NGOs and governmental organizations. Keywords: Change Detection, Forest, GIS, Kalakkad, Mundanthurai, NDVI, Remote Sensing, Vegetation 1. Introduction Remote Sensing has been used in monitoring vegetation coverage for years. Their advancement from time to time, enables more accurate results in vegetation calculation. Hence, satellite based land use coverage has become more reliable than manual ground based interpretation. Forest cover of regions has been forced to degradation in many parts of the world due to climate changes and man-made factors. Therein arises the necessity to constantly monitor the vegetation in forest regions through the available satellite data. The most sought technique in identifying the deforestation or afforestation is change detection technique. Change detection is a process of identifying changes in the state of an object or phenomenon by observing images at different times 1. Also, it can be explained as identification of the change or disruptions of an object through multi-temporal images. The goal of change detection techniques in forestry is detecting the, 1. Pattern of forest cover change, 2. Processes of forest cover change, and 3. Human response to forest covers change 2. Various studies indicate that decline in forest cover can be mainly due to two reasons, human settlement like timbering and climatic factors like global warming. Kalakkad Mundanthurai Tiger Reserve (KMTR) has been a major hotspot for biodiversity in India. With its presence of various flora and fauna, most of the previous studies in the region were directed towards identifying different species of the flora and fauna. Hence, this work *Author for correspondence
Change Detection of Forest Vegetation using Remote Sensing and GIS Techniques in Kalakkad focuses on identifying the decline and incline in forest cover in the KMTR Region 3. 2. Materials and Methods 2.1 Study Area The Kalakkad Mundanthurai Tiger Reserve is situated in Tirunelveli District of Tamil Nadu, India shown in Figure 1. The reserve has been identified as type-1 Tiger Conservation Unit representing the tropical moist evergreen forests worldwide. The region is present in the South-western Ghats and also forms a part in the Agasthiyamalai Biosphere Reserve. The location of the region extents from 80 25 N to 80 53 N latitude and 77 10 E to 77 35 E longitude. KMTR lies in the range of 40-1800 m above MSL. The entire area is about 895 sq.km including Agasthiyamalai, the core zone of the region. KMTR region is the home for various flora and fauna with the Tigers and Lion-Tailed Macaque being a specialty in the region. Fourteen rivers originate from this reserve, which feed numerous tanks in the plains and eleven dams in and around the reserve with three hydroelectric power stations. In this work, we include a buffer zone of 0.05 radial arc degree along the perimeter of the KMTR region. This enables us to understand the vegetation along the boundary of KMTR. The total area after buffering is 1982.2 sq.km. 2.2 Data Used We use Landsat-7 ETM+ data shown in Figure 2(a) and Landsat-8 OLI/TIRS data shown in Figure 2(b) for this work. Both the satellites have the same orbital parameters and hence the inclination of the sensor over the swath is the same. So, multi-temporal images (2005 and 2015) of the region of interest will have same geographical coordinates 4. Instead of using top sheets to extract the region of interest, shape file of the region were acquired from National Biodiversity Portal. This data was used to obtain vegetation index in the region. Figure 1. Location map of KMTR. Figure 2. Landsat Image of KMTR (a) ETM+ 2005. 2 Vol 9 (30) August 2016 www.indjst.org
S. Amar Balaji, P. Geetha and Soman K. P. processes convert the DN to surface reflectance values 6-8. The preprocessing techniques are performed using ENVI software. Figure 2. Landsat Image of KMTR (b) OLI 2015. 2.3 Preprocessing Landsat 7 ETM+ experienced a sensor failure which led to 22 percent loss of data in the form of dark stripes in the image. Hence, these no value (dark) stripes have to be filled with data using the mask bands provided along with the downloaded data from Earth Explorer website. QGis platform was used to fill the no data region in the obtained imagery 5. The data acquired has to be pre-processed before using it for analysis. Original data will have radiance and atmospheric effects in it. So, we have to convert the DN to surface reflectance data for subjecting to NDVI analysis. Hence the data is subjected to radiometric correction and atmospheric correction. Radiometric correction enhances the brightness value range in the digital imagery. The errors and anomalies in the brightness value can lead to misinterpretation of digital image while analysis and hence the radiometric correction. Atmospheric correction removes the atmospheric haze in the digital image. It is done so by normalizing each frequency band in the image. This digitization 3. Results and Discussion 3.1 Analysis and Interpretation The preprocessed data are put to Normalized Differential Vegetation Index (NDVI) analysis in ENVI. NDVI gives the vegetation cover present in the area of interest, in the satellite imagery. NDVI value usually ranges from 0 to 1. The results obtained are classified based on the NDVI range for various vegetation parameters. Based on studies, it is known that range of 0.3 to 0.5 indicates moderate vegetation i.e., shrubs, bushes, smaller plants etc. range of 0.5 to 0.7 states dense vegetation of forest and 0.7 and above indicate very high density of tropical forest 9-11. Based on the above classification parameters the NDVI data was classified for the multi-temporal images as moderate vegetation dense vegetation and very dense vegetation. The change detection is carried out for multi-temporal images of time period 10 years. After acquiring the different vegetation images, the results were subjected to change detection analysis through image difference technique. The analysis yielded the imagery displaying the increase and decrease of different vegetation in Area Of Interest (AOI). Also, the results were obtained in the form of vector data, which enables to identify the total area of each type of vegetation and their changes over the years. 3.1.1 Moderate Vegetation In 2005, the density of the vegetation was high along the boundary of KMTR. It was scarce inside the region and scattered shown in Figure 3(a). However, recent imagery shows that the vegetation along the boundary has slightly increased slightly outside the boundary and considerably improved inside the region of interest shown in Figure 3(b). Moderate vegetation has spread in the intermediate altitudes throughout the region, and has had a considerable amount of declination outside the boundary in the northern and southern region. Overall the average increase in moderate vegetation was found to be 129.74 sq.km and decrease of 108.39 sq.km, in the span of 10 years. The total coverage of Vol 9 (30) August 2016 www.indjst.org 3
Change Detection of Forest Vegetation using Remote Sensing and GIS Techniques in Kalakkad moderate vegetation has increased from 369.02 sq.km to 408.25 sq.km. (a) (b) (c) Figure 3. Moderate Vegetation in KMTR region (a) 2005 (b) 2015 and (c) Increase (blue) and decrease (red) of moderate vegetation 3.1.2 Dense Vegetation The vegetation was found to be spread along the downhill region of the higher altitudes along the eastern direction. The vegetation index density was higher in the northern section; eastern section along boundary and the southern section of KMTR shown in Figure 4(a). But over the years, the spread of vegetation has been throughout and is evenly scattered. Earlier, it was scattered mostly at higher altitudes at northern and southern section, but as of now the spread has been throughout covering the entire higher altitude section and also over the downhill side inside the KMTR shown in Figure 4(b). Dense vegetation has had a large amount of declination in the north-west region and southern region in the higher altitudes. However, it shows inclination at certain places in higher altitudes. The increase and decrease of dense vegetation from 2005 to 2015 has been 330.72 sq.km and 372.23 sq.km respectively. The total are of this vegetation in 2005 was 813.73 sq.km while it is 803.23 sq.km in 2015, indication a slight decrease in the coverage. 3.1.3 Very Dense Vegetation The northwestern and southwestern regions of higher altitudes are found to have this class of vegetation. The downhill region also accommodates scarcely this class of vegetation shown in Figure 5(a). The 2015 image shows, the spread of vegetation evenly spread in the higher altitudes. Other regions have had considerable decrease in the vegetation shown in Figure 5(b). On the whole, analysis shows a good amount of increase of very dense vegetation in the region which adds to the ecological richness of the tiger reserve. On analysis with the vector data, it was found that very dense vegetation had an increase in its spread with 304.29 sq.km and a decrease of 297.97 sq.km. However, on interpreting for the vegetation total coverage, there is an increase from 513.78 sq.km to 572.07 sq.km. 4. Conclusion The results on visual interpretation show that the vegetation as a whole has increase in their spread in the region. Statistical data shows increase in moderate and very dense 4 Vol 9 (30) August 2016 www.indjst.org
S. Amar Balaji, P. Geetha and Soman K. P. (a) (a) (b) (b) (c) (c) Figure 4. Dense Vegetation in KMTR region (a) 2005 (b) 2015 and (c) Increase (blue) and decrease (red) of dense vegetation Vol 9 (30) August 2016 www.indjst.org Figure 5. Very Dense Vegetation in KMTR region (a) 2005 (b) 2015 and (c) Increase (blue) and decrease (red) of very dense vegetation. 5
Change Detection of Forest Vegetation using Remote Sensing and GIS Techniques in Kalakkad Table 1. Type of Vegetation Change of area for different vegetation Area (sq.km) 2005 2015 Inc / Dec Moderate 369.02 408.25 Increase Dense 813.73 803.23 Decrease Very Dense 513.78 572.07 Increase Table 2. Total change in area between 2005 and 2015. Vegetation Area (sq.km) Moderate Increase 129.74 Decrease 108.387 Dense Increase 330.721 Decrease 372.53 Very Dense Increase 304.29 Decrease 297.97 vegetation and decrease in dense vegetation. It is shown in Table 1. Table 2 shows the results obtained after image differentiation between 2005 and 2015 NDVI imagery. The above table indicates that there has been both deforestation (decrease) and afforestation (increase) occurring in the KMTR buffer area naturally, with afforestation i.e., increase in spread of vegetation occurring for both moderate and very dense vegetation. However, the location of spread of this afforestation (increase) and deforestation (decrease) in the KMTR region from the image differentiation maps Figure 3(c), Figure 4(c) and Figure 5(c) suggest that the vegetation as a whole have moved towards the higher altitudes in the region owing to the increased global warming effects. Spread of very dense vegetation and dense vegetation have moved further interior in the mountain ranges. Moderate vegetation which was found covering the outer regions of KMTR during 2005 has dominated their spread towards the mountain ranges constantly. 5. References 1. Singh A. Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing. 1989; 10(6):989 1003. 2. Lambin EF, Baulies X, Bockstael N, Fischer G, Krug T, Leemans R. Land-Use and Land-Cover Change (LUCC) Implementation Strategy. 1999. 3. Lindquist EJ, Hansen MC, Roy DP, Justice CO. The suitability of decadal image data sets for mapping tropical forest cover change in the Democratic Republic of Congo: implications for the global land survey. International Journal Remote Sensing. 2008; 29(24):7269 75. 4. Xu D, Guo X. Compare NDVI extracted from Landsat 8 imagery with that from Landsat 7 imagery. American Journal of Remote Sensing. 2014 Sep; 2(2):10 4. 5. Chen J, Zhu X, Vogelmann JE, Gao F, Jin S. A simple and effective method for filling gaps in Landsat ETM+ SLCoff images. Remote Sensing of Environment. 2011 Apr; 115(4):1053-64. 6. Forkuo EK, Frimpong A. Analysis of Forest Cover Change Detection. International Journal of Remote Sensing Applications. 2012 Dec; 2(4):1-11. 7. Turner DP, Cohen WB, Kennedy RE, Fassnacht KS, Briggs JM. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment. 1999 Oct; 70(1):52 68. 8. Zafar SM. Spatio-temporal analysis of land cover/land use changes using geo-informatics: A case study of Margallah Hills National Park. Indian Journal of Science and Technology. 2014 Nov; 7(11):1832 41. 9. Mas JF. Monitoring land-cover changes: A comparison of change detection techniques. International Journal of Remote Sensing. 1999; 20(1):139 52. 10. Maselli F. Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data. Remote Sensing of Environment. 2004 Feb; 89(4):423 33. 11. Canty MJ. Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/ IDL and Python. CRC Press; 2014 Jun. 6 Vol 9 (30) August 2016 www.indjst.org