Chronological Change of Land Use/Land Cover of the Muhuri River Basin from 1972 to 2016, Tripura, North-East India

ISSN (Print) : 0974-6846 Indian Journal of Science and Technology, Vol 10(22), DOI: 10.17485/ijst/2017/v10i22/112067, June 2017 ISSN (Online) : 0974-5645 Chronological Change of Land Use/Land Cover of the Muhuri River Basin from 1972 to 2016, Tripura, North-East India

Jatan Debnath*, Nibedita Das, Istak Ahmed and Moujuri Bhowmik Department of Geography and Disaster Management, Tripura University, Suryamaninagar, Tripura West, Agartala – 799022, Tripura, India; [email protected], [email protected], [email protected], [email protected]

Abstract Background/Objective: To present an investigation on the land use/land cover change of the Muhuri River basin with the help of Normalized Difference Vegetation Index (NDVI) analysis from the year 1972 to 2016. Methodology/ Analysis: The remotely based satellite images have been used to realise the LULC change of the study area. Within the period of 44 years (1972-2016), especially, the dense forests and open forests have been decreased by

gained 6.66 percent and 24.91 percent respectively. The NDVI results have articulated the fact that healthy vegetation 93.17% and 77.54% respectively, whereas the Findings:main driving factors like settlement and rubber plantation have significantly from the neighbouring country has mainly hampered the sustainability of the forest cover within the study area, whereas, theirwith highsubsequent DN value interferences has declined hadsignificantly. changed the existed The land overall cover findings into different of the research land use indicates categories that asthe settled immigrants area, agricultural land or rubber plantation; increase in degraded forest area becomes alarming for the life of the River Muhuri. Novelty/Improvement:

Above all, the study has analysed some specific changes in the basin environment and raised the finger towards the sustainable use of the natural resource. Keywords: Accuracy, Forest Cover, Land Use/Land Cover, NDVI 1. Introduction has been depleted significantly due to the interferences of the human population13,14. Moreover, shifting cultivation, Long term observation of land use/land cover change is which is regarded as the oldest cultivation method very much significant to realise the ultimate change, as practiced in tropical hilly areas15, plays a primary role well as to predict the future scenario of a given basin and in conversion of natural land cover into degraded forest planning for managing the natural resource of the area1–4. in the upper catchment, leading to an effect on water In addition, it can be some idea about the dimension of the discharge16–18. forest cover in river basin which regulates the hydrological Therefore, multi-temporal remote sensing images cycle; as well as evaluate the local environment condition become the most reliable data sources that provide the throughout the basin5–8. This land use/land cover change valuable information about land use/land cover change of technique has gained global importance as it analyses the a given area at a time scale19–25. More specifically, this cost LULC change in spatio-temporal scale and also reflects effective data source provides forest data as accurately the interferences of the human population on a specific as possible26,27. In addition, vegetation health can also be environment9,10. identified from the satellite images using the Normalized Biologically rich forest provides timber and medicinal Difference Vegetation Index (NDVI). It becomes a widely facility along with firewood to the rural communities11,12 accepted method to study the vegetation indices28, as well and therefore, such kind of rich tropical forest ecosystem as, this time series data also provides the land surface

* Author for correspondence Chronological Change of Land Use/Land Cover of the Muhuri River Basin from 1972 to 2016, Tripura, North-East India

response with the change of climate and the effect of data of 1972 as the base year and 2016 as the recent year. human interferences29,30. Nowadays, combining with the In order to comprehend the change, the conversion of the modern Geoinformatics environment i.e., ArcGis, Erdas, LULC from 1972—1985—1995—2005-- 2016 and from Envi, one can gain relatively more accurate result on land 1972 to 2016 have been analysed. In addition, the present use/land cover change in contrast to the Ground reference study aims to specify the change of the forest covered data. (LC) area from 1972 to 2016 using the NDVI. The study Tripura is one of the eight sisters among the North- was entirely conducted in the GIS environment using eastern states of India which belongs to the rich satellite imageries. biodiversity hotspot zone of India. However, major influx of population from neighbouring Bangladesh (erstwhile 2. Description of the Study Area East Pakistan), before and after independence, created major change in land use/land cover pattern of this State The Muhuri River basin of Tripura, India is located in general and in the Muhuri River basin in particular. between 91˚26ʹ E to 91˚44ʹ E longitude and 23˚10ʹ N to Natural forests were converted to build up areas and 23˚25ʹ N latitude Figure 1 with an area extent of 701 km2 agricultural lands. and belongs to the undulating highlands with adjacent The researcher has mainly analysed the land use/land narrow valleys. The elevation of the study area ranges cover change of the Muhuri River basin using the MSS between 15m to 180m. The River Muhuri originates from

Figure 1. Location map of the study area.

2 Vol 10 (22) | June 2017 | www.indjst.org Indian Journal of Science and Technology Jatan Debnath, Nibedita Das, Istak Ahmed and Moujuri Bhowmik the Baramura-Deotamura Range and enters Bangladesh acceptable classifier. Hence, in the present study, the after flowing for 59 km within Tripura. Its basin maximum likelihood algorithm was used along with the experiences moist humid climate with 2,300 mm annual supervised classification. rainfall, most of which occurs during monsoon season; 21˚C to 38˚C temperature is recorded in summer and 3.3 Accuracy Assessment 4˚ C to 32˚C in winter season. The basin has a common Assessment of accuracy is very much important in terms boundary with Bangladesh and immigration is a general of accurateness of the land use/land cover map prepared phenomenon. The study area has a total population of 1, on the basis of satellite images. Considering the accuracy, 80,000among which the migrated Bengali community it is mainly expressed the degree of correctness of a dominates over the indigenous tribal communities. classification map32 along with statistical application. For Recently, this part of Tripura becomes rich in rubber this purpose high resolution Goggle earth imageries were plantation and in upper catchment of the Muhuri River used while field verification, using GPS, was carried out. bamboo is common natural vegetation. Confusion matrix was used for classification accuracy where producer’s accuracy, user’s accuracy, Overall 3. Methodology accuracy and Kappa accuracy were calculated using this matrix table. The overall accuracy is mainly calculated by 3.1 Image Processing dividing the total correctly classified pixels by the total The study of LULC change detection and its evaluations number of pixels in the error matrix33 whereas Kappa was performed by adopting a series of steps and processes co-efficient mainly determines the degree of agreement including data collection, data pre-processing, supervised among the classified map and reference data33. These are classification and post classification comparison. For this calculated using the following formula: purposethe dry season sattelite images were selected, as these images remained cloud free during this period. Overall accuracy: (1) Histogram equalizations and radiomatric corrections were applied using the Arcmap 10.1. Histogram equalization Kappa accuracy: (2) is mainly used for contrast enhancement of the sattelite image so that the pixel values can be distributed uniformly, Source: Congalton 1991 whereas,radiomatric correction ismainly applied to avoid Where is regarded as the number of rows in the the radiomatric distortion and thus reliability of the matrix, express the total number of correctly classified pixels’ brightness value is increased20. All the collected pixels in row and column , and are the data were allocated by the UTM WGS-84, 46N projection marginal totals of row and column respectively, and to create the data harmonizingwith each other. Since the is the total number of pixels in the matrix table. resolution of the 1972 MSS data was 60 meter, therefore, it was converted into 30 meter using the resampling tool. 3.4 Change Detection Moreover, image to image registrationwas also appliedto Post Classification Comparison (PCC) method is remove the error during overlaying and change detection considered as a most prolific comprehensive method analysis. for change detection analysis. In this method year-wise land use/land cover change, as well as conversion of one 3.2 Image Classification particular land use/land cover class into another class has Modern Geoinformatics environment is mainly been calculated and compared34,35. applied in supervised and unsupervised methods of More specifically, in PCC method each of the particular classification for land use/land cover. The supervised imagery has been classified independently, then overlaid classification is the most widely applied method, as well and compared using the pixel by pixel method and thus as accurate classification algorithm in comparison to the Land use/land cover change map, also called as ‘From- the unsupervised method30,31. Moreover, the maximum to’ map36–38 has been established. likelihood algorithm is considered to be one of the most

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3.5 Normalised Difference Vegetation Index 4. Results and Discussion (NDVI) The creations of NDVI map from the satellite images 4.1 Accuracy Assessment becomes more supportive work for land use/land cover Regarding accuracy of the LULC maps of the Muhuri classification. This map is mainly developed using the red River basin, producer’s accuracy, user’s accuracy, overall and near-infrared reflectance ratios, which usually provide accuracy and Kappa co-efficient have been calculated the vegetation data with reference to the reflectance of for five different years. The classified images showed the the ground surface pixel39,40. The Normalised Difference overall accuracy of 85, 97, 87, 84 and 98 percent in the Vegetation Index is calculated using the following year 1972, 1985, 1995, 2005 and 2016 with kappa co- formula: efficient of 84, 96, 86, 83 and 97 percent respectively Table 1. In the LULC map of the year 1972, different categories NDVI= (NIR - red) / (NIR + red) (3) of forests, shifting cultivation and settlement have been classified more accurately, whereas, agricultural land and Where, red corresponds to landsite TM band 3, landsat water bodies illustrate confused. In case of 1985 map, all MSS band5 and landsat OLI band 4; NIR corresponds the categories have been classified more or less accurately to landsat TM band 4, MSS band7 and OLI band 5. The and as a result the overall accuracy of the map has reached NDVI was developed using the Arcgis 10.1 software. Its to 97 percent. On the other hand, the LULC map of 1995 value is always confined within -1 to +1. Here, the positive showed88 percent of overall accuracy, because areas DN values (+0.01 to +1) indicate the presence of healthy under open forest, shifting cultivation and agriculture vegetation and negative values (-0.01 to -1) indicate the have displayed some confusion with the other remaining absence of vegetation. Moreover, confirmation about classes. Similarly, in the classified map of 2005, the the open forest, dense forest and degraded forest of a percentage of overall accuracy has remained as 85, which particular place and its long term change detection could is quite less than that of the other classified maps. Here, be improved considering the DN values of the NDVI map dense forest, degraded forest and rubber plantation have of that particular study area41. bewildered highly in comparison with the other classified Table 1. Accuracy assessment of the LULC map, 1972-2016 1972 LULC DF OF DGF AL SC SM WB BL Total PA UA DF 94 4 0 0 0 0 0 0 98 94 95.92 OF 6 91 1 0 0 0 0 0 98 95.79 92.86 DGF 0 0 73 13 1 1 0 0 88 82.02 82.95 AL 0 0 1 96 5 1 5 4 112 70.59 85.71 SC 0 0 0 5 78 0 2 0 85 86.67 91.76 SM 0 0 14 0 0 30 0 0 44 93.75 68.18 WB 0 0 0 22 6 0 33 0 61 82.5 54.10 BL 0 0 0 0 0 0 0 25 25 86.21 100 Total 100 95 89 136 90 32 40 29 611 Overall accuracy 86% Kappa accuracy 85% 1985 LULC D F OF DGF AL SC SM WB WL RC Total PA D F 61 0 0 0 0 0 0 0 2 63 94 OF 0 66 0 0 0 0 0 0 2 68 100 DGF 0 0 67 0 5 0 0 0 1 73 88 AL 0 0 5 59 1 1 2 0 0 68 78 SC 0 0 4 2 55 0 0 0 0 61 89 SM 0 0 0 4 0 32 1 0 0 37 97 WB 0 0 0 11 0 0 47 0 0 58 94 WL 0 0 0 0 1 0 0 17 0 18 100 RC 4 0 0 0 0 0 0 0 61 65 92 Total 65 66 76 76 62 33 50 17 66 511

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Overall accuracy 97% Kappa accuracy 96% 1995 LULC D F OF DGF AL SC SM WB WL RC Total PA D F 93 2 0 0 0 0 0 0 0 95 100 OF 0 75 5 6 0 0 0 0 1 87 80.65 DGF 0 0 87 6 18 1 1 0 0 113 94.57 AL 0 0 0 83 5 2 0 2 0 92 76.15 SC 0 0 0 0 72 0 0 1 0 73 69.90 SM 0 0 0 13 0 34 0 0 0 47 91.89 WB 0 0 0 1 8 0 41 0 0 50 97.62 WL 0 0 0 0 0 0 0 36 0 36 92.31 RC 0 16 0 0 0 0 0 0 97 113 98.98 Total 93 93 92 109 103 37 42 39 98 706 Overall accuracy 88% Kappa accuracy 87% 2005 LULC D F OF DGF AL SC SM WB WL RC Total PA UA D F 66 0 0 0 0 0 0 0 23 89 71.74 74.16 OF 0 87 1 0 0 0 0 0 0 88 96.67 98.86 DGF 0 3 77 2 0 1 0 0 0 83 75.49 92.77 AL 0 0 9 44 1 3 1 1 0 59 83.02 74.58 SC 0 0 6 6 76 0 0 0 0 88 98.70 86.36 SM 0 0 9 1 0 29 0 0 0 39 87.89 74.36 WB 0 0 0 0 0 0 31 0 0 31 96.88 100 WL 0 0 0 0 0 0 0 19 0 19 95 100 RC 26 0 0 0 0 0 0 0 71 97 75.53 73.20 Total 92 90 102 53 77 33 32 20 94 593 Overall accuracy 85% kappa accuracy 84% 2016 LULC D F OF DGF AL SC SM WB WL RC Total PA UA D F 75 0 0 0 0 0 0 0 0 75 99 100 OF 3 73 0 3 0 0 0 0 0 79 95 92 DGF 0 0 74 0 3 0 0 0 0 77 96 96 AL 0 0 0 79 0 15 0 2 0 96 94 82 SC 0 0 0 0 54 0 0 0 0 54 93 100 SM 0 0 3 0 0 31 0 0 0 34 97 91 WB 0 0 0 10 0 0 48 0 0 58 100 83 WL 0 0 0 0 0 0 0 49 0 49 96 100 RC 0 4 0 2 1 0 0 0 70 77 100 91 Total 78 77 77 94 58 46 48 51 70 599 Overall accuracy 98% kappa accuracy 97% DF- Dense forest, OF- Open forest, DGF- Degraded forest, AL- Agricultural land, RP- Rubber plantation, SC- Shifting cultivation, SM- Settlement, WB- Water body, BL- Barren land. elements; as a result, the percentages of overall accuracy 4.2 Image Classification as well as Kappa accuracy became lesser than the other LULC classifications of all the study years have been years’. Moreover, the overall accuracy and kappa accuracy made using the supervised classification technique. The percentages of 2016 classified map attained 98 and 97 base map of1972 has been classified into eight LULC percent respectively as all the classified categories have categories viz. dense forest, open forest, degraded forest, been classified accurately. Although there has remained cultivated land, shifting cultivation, settlement, water confusion between the pixels of each other class in all body and barren land Figure 2 and the results revealed the classified maps but classification accuracy above the areas occupied by these respective classes as 6.87, 85percent is reliable for further analysis42. 39.93, 39.54, 5.01, 4.14, 2.37, 2.09 and 0.06 percent. Again,

Vol 10 (22) | June 2017 | www.indjst.org Indian Journal of Science and Technology 5 Chronological Change of Land Use/Land Cover of the Muhuri River Basin from 1972 to 2016, Tripura, North-East India

Figure 2. Land use/ land cover of the Muhuri river basin during 1972, 1985, 1995, 2005 and 2016.

the maps of the remaining years of 1985, 1995, 2005 and 1.28, 6.66, 1.45 and 0.32 respectively. Rubber plantation 2016 have shown rubber plantation as an additional LU occupied about 24.91 percent area of the studied basin in category which was absent earlier in the basin area. The that particular year. The category-wise areal coverage and recent map of 2016 indicated the percent of areal coverage percent of coverage during all the studied periods have by different LULC categories as 0.47, 8.97, 44.8, 11.44, been displayed in Table 2.

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Table 2. Area under different LULC categories of the Muhuri river basin (1972-2016) 1972 1985 1995 2005 2016 LULC Types Area Area Area Area Area Area Area Area Area Area (km2) (%) (km2) (%) (km2) (%) (km2) (%) (km2) (%) Dense forest 48.2 6.87 41.4 5.90 42.53 6.06 28.37 4.04 3.29 .47 Open forest 280.2 39.93 200.63 28.59 175.36 24.99 178.86 25.49 62.93 8.97 Degraded forest 277.43 39.54 328.92 46.87 311.9 44.45 265.18 37.799 312.3 44.50 Agricultural land 35.16 5.01 70.14 10 75.71 10.79 79.27 11.30 80.29 11.44 Rubber Plantation 0 0 10.28 1.46 32.43 4.62 82.04 11.69 174.8 24.91 Shifting cultivation 29.03 4.14 12.02 1.71 16.6 2.37 15.5 2.21 8.98 1.28 Settlement 16.64 2.374 23.93 3.41 34.39 4.90 41.54 5.92 46.75 6.66 Water body 14.64 2.09 13.65 1.95 12.65 1.80 10.67 1.52 10.15 1.45 Barren land 0.42 0.06 0.75 0.11 0.15 0.02 0.29 0.04 2.23 0.32 Total 701.72 701.72 701.72 701.72 701.72

4.3 Trend in Land Use and Land Cover rubber plantation (215.47%), shifting cultivation (38.10%) Change and settlement (43.71%). On the other hand, water body The temporal data sets of land use/land cover of the study and barren land had been reduced by 7.33 and 80 percent area have indicated some considerable changes from the respectively. base period to the respective selected periods and that The assessment of the next 10 years period revealed have been inflated by human as well as natural factors. that the area under dense forest and degraded forest During the 1972-1985 periods of 13 years, the areas under had decreased by 33.29 and 14.98 percent, while open dense forest and open forest had been decreased by 14.11 forest had increased by 2 percent. Like previous periods and 28.40 percent respectively, whereas, degraded forest agricultural land, rubber plantation and settlement had and agricultural land had been increased significantly increased by 4.70, 152.97 and 20.79 percent respectively. by 18.56 and 99.49 percent respectively. Moreover, area On the other hand, area under shifting cultivation and under shifting cultivation had decreased by 58.59 percent water body had decreased by 6.63 and 15.65 percent and settled area had increased appreciably by 43.81 respectively whilst barren land had increased by 93.33 percent with reduction in water body by 6.76 percent. percent. During this period the basin had a significant increase in The study revealed that during the recent periods of barren land by 78.57 percent Table 3. 11 years between 2005 and 2016, area under dense forest On contrary, in 1985-1995 periods of 10 years, dense and open forest have been decreased by 88.40 and 64.82 forest had increased by 2.73 percent, whereas both the percent respectively, while degraded forest, agricultural open forests and degraded forests had declined by 12.60 land, rubber plantation and settlement have increased and 5.17 percent respectively due to the significant by 17.77, 1.29, 113.07 and 12.54 percent respectively. increase in agricultural land (7.94%), newly introduced Moreover, this study period also experienced a decrease Table 3. Relative changes in land use/land cover of the Muhuri river basin (1972-1985-1995-2005-2016 and 1972-2016) 1972-1985 1985-1995 1995-2005 2005-2016 1972-2016 LULC Types Area Area Area Area Area Area Area Area Area Area (km2) (%) (km2) (%) (km2) (%) (km2) (%) (km2) (%) Dense forest -6.8 -14.11 1.13 2.73 -14.16 -33.29 -25.08 -88.40 -44.91 -93.17 Open forest -79.57 -28.40 -25.27 -12.60 3.5 2 -115.93 -64.82 -217.27 -77.54 Degraded forest 51.49 18.56 -17.02 -5.17 -46.72 -14.98 47.12 17.77 34.87 12.57 Agricultural land 34.98 99.49 5.57 7.94 3.56 4.70 1.02 1.29 45.13 128.36 Rubber Plantation 10.28 Absent 22.15 215.47 49.61 152.97 92.76 113.07 174.8 Shifting cultivation -17.01 -58.59 4.58 38.10 -1.1 -6.63 -6.52 -42.06 -20.05 -69.07 Settlement 7.29 43.81 10.46 43.71 7.15 20.79 5.21 12.54 30.11 180.95 Water body -0.99 -6.76 -1 -7.33 -1.98 -15.65 -0.52 -4.87 -4.49 -30.67 Barren land 0.33 78.57 -0.6 -80 0.14 93.33 1.94 668.96 1.81 430.95

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of 4.87 percent water body and significant increase in 4.4 Trend in Alteration of Land Use/Land barren land by 668.96 percent. Cover Categories Hence, the overall study period of 44 years (1972- To signify the alteration of LULC of the study area, five 2016) experienced a significant change in land use/ change maps have been obtained by overlaying the maps land cover of the Muhuri River basin of Tripura. Dense of (i) 1972 and 1985, (ii) 1985 and 1995, (iii) 1995 and forest and open forest have been decreased tremendously 2005, (iv) 2005 and 2016, and (v) the past (1972) and the by 93.17 and 77.54 percent respectively, whereas, present (2016). The change maps have been displayed degraded forest, agricultural land and settlement have as ‘From-to’ map. Every change map gives information increased significantly by 12.57, 128.36 and 180.95 about the changed and unchanged areas that have been percent respectively. In addition to it, water body have marked using different colours Figure 3, Figure 4, Figure decreased by 30.67 percent and barren land has increased 5 and Figure 6. considerably by 430.95 percent.

DF- Dense forest, OF- Open forest, DGF-Degraded forest, AL- Agricultural land, RP- Rubber plantation, SC- Shifting cultivation, SM- Settlement, WB- Water body, BL- Barren land. Figure 3. Conversion of different LULC categories during 1972-1985 periods.

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DF- Dense forest, OF- Open forest, DGF- Degraded forest, AL- Agricultural land, RP- Rubber plantation, SC- Shifting cultivation, SM- Settlement, WB- Water body, BL- Barren land. Figure 4. Conversion of different LULC categories during 1985-1995 periods.

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Degraded forest, AL- Agricultural land, RP- Rubber plantation, SC- Shifting cultivation, SM- Settlement, WB- Water body, BL- Barren land. Figure 5. Conversion of different LULC categories during 1995-2005 periods.

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Agricultural land, RP- Rubber plantation, SC- Shifting cultivation, SM- Settlement, WB- Water body, BL- Barren land. Figure 6. Conversion of different LULC categories during 2005-2016 periods. • The ‘from-to’ map developed from 1972-1985 30.27 percent remained unchanged. Similarly, in indicates significant conversion Figure 3 and Table case of degraded forest, about 4.94 percent area 4. It has been noticed that dense forest area has been was transformed to agricultural land, 58.73 and highly converted into open forest (33.17%), degraded 16.36 percent area became open forest and dense forest (25.59%), plantation (17.73%), agriculture forest respectively and 5.11 percent was replaced by (0.85%), settlement (0.52%) and shifting cultivation settlement; whereas 11.22 percent degraded forest (0.25%), and about 21.78 percent remained remained unaffected during this study period. unchanged. The open forest area has been replaced However, most of the agricultural land remained by dense forest (7.29%), degraded forest (46.85%), unchanged and the rest 10.58 percent area was plantation (9.92%), settlement (2.18%), and about occupied by settlement; while in case of water body

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Table 4. Conversion area (m2) under LULC change maps of the study area 1972-1985 1985-1995 1995-2005 2005-20016 1972-2016 From-To Area (Km2) Area (%) Area (Km2) Area (%) Area (Km2) Area (%) Area (Km2) Area (%) Area (Km2) Area (%) UC. DF 9.72 21.78 18.43 50.33 9.03 23.38 1.08 4.45 0.97 2.18 DF_OF 14.8 33.17 10.13 27.66 10.7 27.71 5.36 22.08 8.66 19.48 DF_DGF 11.42 25.59 6.42 17.53 6.69 17.32 8.646 35.62 22.59 50.82 DF_AL 0.38 0.85 2.02 0.05 0.44 1.14 0.18 0.74 0.26 0.58 DF_RP 7.91 17.73 1.31 3.58 10.1 26.15 8.348 34.40 10.62 23.89 DF_SC 0.11 0.25 0.06 0.16 1.63 4.22 0.1 0.41 0.43 0.97 DF_SM 0.23 0.52 0.1 0.27 0.03 0.08 0.53 2.18 0.89 2.00 DF_WB 0.01 0.02 0.15 0.41 0 0 0.01 0.04 0.02 0.04 DF_BL 0.04 0.09 0 0 0 0 0.02 0.08 0.01 0.02 OF_DF 20.14 7.29 8 5.30 11.04 6.08 0.68 0.40 1.51 0.55 UC. OF 83.6 30.27 88.51 58.65 73.45 40.45 25.55 15.17 33 11.94 OF_DGF 129.38 46.85 46.19 30.61 55.01 30.30 84.12 49.947 130.49 47.23 OF_AL 8.55 3.10 1.71 1.13 7.51 4.14 2 1.19 12.47 4.51 OF_RP 27.4 9.92 2.33 1.54 26.46 14.57 47.6 28.26 79.6 28.81 OF_SC 0.75 0.27 2.67 1.77 6.27 3.45 1.76 1.04 2.93 1.06 OF_SM 6.01 2.18 0.84 0.56 1.82 1.00 6.25 3.71 14.99 5.43 OF_WB 0.04 0.01 0.63 0.42 0.01 0.01 0.17 0.10 0.6 0.22 OF_BL 0.29 0.11 0.02 0.01 0 0 0.3 0.18 0.69 0.25 DGF_DF 8.09 2.87 3.99 1.16 4.83 1.51 0.32 0.11 0.49 0.17 DGF_OF 46.13 16.36 56.47 16.45 78.22 24.40 14.56 5.23 16.83 5.97 UC.DGF 165.65 58.73 226.98 66.14 164.78 51.41 128.93 46.30 128.56 45.63 DGF_AL 31.65 11.22 15.5 7.43 20.86 10.25 15.97 13.28 37.33 13.25 DGF_RP 13.93 4.94 7.096 2.07 16.22 5.06 68.13 24.47 70.94 25.18 DGF_SC 1.46 0.52 2.21 0.64 4.28 1.34 2.44 0.88 2.25 0.80 DGF_SM 14.42 5.11 19.53 5.69 19.03 5.94 25.3 9.09 23.45 8.32 DGF_WB 0.45 0.16 1.36 0.40 0.27 0.08 1.04 0.37 0.78 0.28 DGF_BL 0.26 0.09 0.04 0.01 0.05 0.02 0.75 0.27 1.1 0.39 AL_DF 0.27 0.80 0.13 0.20 0.06 0.08 0.01 0.017 0.01 0.03 AL_OF 1.36 4.03 2.92 4.44 1.24 1.75 1.4 1.97 0.84 2.49 AL_DGF 12.33 36.54 13 19.77 26.94 38.12 29.75 41.77 8.71 25.86 UC. AL 15.43 45.73 37.78 57.45 25.57 36.18 21.66 30.41 15.61 46.35 AL_RP 0.49 1.45 0.05 0.08 0.2 0.28 8.88 12.47 3.64 10.81 AL_SC 0.93 2.76 1.13 1.72 1.74 2.46 1.03 1.45 0.14 0.42 AL_SM 2.22 6.58 9.823 14.94 14.03 19.85 7.56 10.62 4.11 12.20 AL_WB 0.66 1.96 0.863 1.31 0.71 1.00 0.2 0.28 0.41 1.22 AL_BL 0.05 0.15 0.06 0.09 0.18 0.25 0.73 1.02 0.21 0.62 SC_DF 1.65 5.92 0.05 1.45 0.02 0.34 0.09 0.64 0.13 0.47 SC_OF 7.52 27.00 0.6 17.34 1.73 29.62 1.46 10.38 2.37 8.53 SC_DGF 9.45 33.93 0.49 14.16 1.46 25 8.58 61.02 14.49 52.14 SC_AL 5.38 19.32 1.9 54.91 1.49 25.51 1.16 8.25 5.04 18.14 SC_RP 2.26 8.11 0 0 0.14 2.40 2.1 14.94 3.79 13.64 UC.SC 0.42 1.51 0.17 4.91 0.37 6.34 0.33 2.35 0.38 1.37 SC_SM 0.89 3.20 0.18 5.20 0.61 10.45 0.3 2.13 1.45 5.22 SC_WB 0.18 0.65 0.06 1.73 0 0 0.03 0.21 0.1 0.36 SC_BL 0.1 0.36 0.01 0.29 0.02 0.34 0.01 0.07 0.04 0.14 WB_DF 0.25 3.09 0 0 0.06 1.82 0 0 0.03 0.37 WB_OF 0.81 10.02 0.08 5.23 0.78 23.71 0 0 0.32 3.99 WB_DGF 2.09 25.87 0.25 16.34 1.42 43.16 0.21 14.09 2.5 31.13 WB_AL 3.71 45.92 0.64 41.83 1.36 10.94 1.48 32.21 3.72 46.33 WB_RP 0.3 3.71 0 0 0.2 6.08 0.01 0.67 0.62 7.72 WB_SC 0.34 4.21 0 0 0.07 2.13 0 0 0.05 0.62 WB_SM 0.3 3.71 0.25 16.34 0.09 2.74 0.22 14.78 0.63 7.85 UC.WB 0.27 3.34 0.31 20.26 0.3 9.12 0.55 36.91 0.14 1.74 WB_BL 0.01 0.12 0 0 0.01 0.30 0.02 1.34 0.02 0.25 UC- Unchanged, DF- Dense forest, OF- Open forest, DGF- Degraded forest, AL- Agricultural land, RP- Rubber plantation, SC- Shifting cultivation, SM- Settlement, WB- Water body, BL- Barren land.

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about 45.92 percent area was replaced by agricultural unchanged. land and only 30% area remained unchanged. It has • The conversion map of 2005 to 2016 recognized also been identified that 33.93 percent area of shifting rubber plantations as the major occupier of other cultivation has been converted into degraded forest. land use/land cover areas Figure 6 and Table • In the 1985-1995 ‘From-to’ map same kind of 4. During this period the dense forest has been alteration prevailed Figure 4 and Table 4. Here both transformed to open forest (22.08%), degraded forest the dense and open forests have predominantly (33.62%), rubber plantation (34.40%) and settlement been transformed. Dense forest was replaced by (2.78%). Open forest has been converted to degraded open forest (27.66%), degraded forest (17.53%), forest (49.95%), rubber plantation (28.26%) and agriculture (0.05%), plantation (3.58%), shifting 3.71% (settlement) while most of the degraded cultivation (0.16%), settlement (0.27%), water body forest remained unchanged (46.30%) and 13.28 and (0.41%), and nearly 50.35 percent area of the dense 24.47 percent area has been occupied by agriculture forest remained unchanged. On the other hand, open and rubber plantation. Similarly 10.62 percent of forest was replaced by dense forest (5.30%), degraded agricultural land has been transformed into settled forest (30.61%), agriculture (1.13%), plantation area and 41.77 percent remained as cropland. The (1.54%), shifting cultivation (1.77%), settlement map also displayed that 61.02 percent area of shifting (0.56%), water body (0.42%), and 58.65percent area cultivation become degraded forest. In case of water of the open forest remained unchanged. This period body, 32.21 and 20 percent areas have been replaced indicated no change in 57.45 percent agricultural by agricultural land and settled area respectively. land and 14.94 percent was altered into settlement. • It is evident from the ‘From-to’ map of the entire The only significant conversion in shifting cultivation study period (44 years) from 1972-2016 Figure 7 was observed where 54.91% area was altered into that the rate of land use/land cover conversion is degraded forest. Similarly, water body was mainly comprehensive in the forest area of the Muhuri River altered to settlements (16.34%) and agricultural land basin. The total areas under dense forest and open (41.83%), and 20.26% remained unchanged. forest have been changed by 93.17% and 77.54%. The • The 1995-2005 conversion maps also revealed aggressive interference of rubber plantation from significant transformation of open and dense forest 0-174.8 km2, expansion of cultivated land and settled areas into other LULC categories Figure 5 and areas have altered the LULC scenario of the study Table 4. The map indicated the conversion of 27.71 area significantly and have amplified the coverage percent dense forest to open forest, 26.15 percent from 35.16 km2-80.26 km2 and 16.6 km2-46.75 km2 to rubber plantation, and 17.32 and 4.22 percent to respectively. degraded forest and shifting cultivation respectively. Whereas, in case of open forest 40 percent remained 4.5 Status of the Normalised Difference unchanged, 30.30 percent became degraded forest Vegetation Index (NDVI) and 14.57, 3.57 and 3.45 percent area were altered by Regarding the NDVI analysis, it is most comprehensively rubber plantation, shifting cultivation and settlement related with the land use/land cover of the study area, respectively. Moreover, in case of degraded forest, particularly, with the forest covered area. Through this 51.41 percent area remained unchanged, whereas Vegetation Index analysis, the natural forest cover, as well 10.25, 5.06 and 5.94 percent area were replaced as area under plantation can be well defined and such by agriculture, rubber plantation and settlement kind of analysis also improves the validity of the research respectively. About 36.18 percent agricultural land work like land use/land cover change. However, it has remained unchanged and 20 percent was replaced been noticed from the year-wise extracted NDVI maps by settlement. Similarly, in case of shifting cultivation that the natural vegetation has been affected adversely in 25.51 and 20.31 percent areas became degraded the study area Figure 8. Although the vegetation scenario forest and dense forest respectively. Area under of the basin was quite good till the year 2005 but 2016 water body was replaced by agricultural land (40%) scenario have exposed the degraded nature of forest of the and settlement (10%) and 20 percent remained study area.

Vol 10 (22) | June 2017 | www.indjst.org Indian Journal of Science and Technology 13 Chronological Change of Land Use/Land Cover of the Muhuri River Basin from 1972 to 2016, Tripura, North-East India

DF- Dense forest, OF- Open forest, DGF- Degraded forest, AL- Agricultural land, RP- Rubber plantation, SC- Shifting cultivation, SM- Settlement, WB- Water body, BL- Barren land. Figure 7. Conversion of different LULC categories during 1972-2016 periods.

14 Vol 10 (22) | June 2017 | www.indjst.org Indian Journal of Science and Technology Jatan Debnath, Nibedita Das, Istak Ahmed and Moujuri Bhowmik

Figure 8. Normalized Difference Vegetation Index (NDVI) analysis of the study area. The vegetation indices maps of 1972, 1985, 1995 in the upper catchment. That situation was gradually and 2005 had crossed the high grid value of 55, which changed in the following years in the entire basin in correspondingly indicates the existence of high to general and in the lower catchment area in particular. The medium density vegetation in the study area. Whereas, in refugee influx from the neighbouring country Bangladesh case of 2016 vegetation index, the high grid value reached during Indian independence (1947) and after partition up to 44 only which indicates low to medium vegetation (during India-Pakistan partition at 1947 which continued in the study area. Eventually, after field verification it can till 1956; 1963 Communal Riot in Bangladesh; 1971 be realised that in the vegetation index map of 2016, only Bangladesh Liberation War) had lead to an alteration of areas under rubber plantation has reflected high DN LC set-up in this part of the basin. The local indigenous value and the areas devoid of any vegetation reflected low people were harassed and evicted from their forest land DN value. and subsequently, it became difficult for them to survive Moreover, the LULC map of 2016 has also been over their own land and thus forced to shift towards the compared with that of the vegetation index map of that upper catchment area. Moreover, the traditional jhumias particular year which reflects the presence of degraded (shifting cultivators) were bound to accept the non- forest in the upper catchment and rubber plantation in traditional jhum activity due to decline of forest land. the lower catchment of the basin. In all the NDVI maps of Earlier in case of the traditional jhooming the fallow the study area the presence of degraded forest in the lower period of a particular jhum plot was for 20-25 years but due catchment has also been highlighted. to huge population pressure the indigenous tribal people have accepted the non-traditional jhooming, where the fallow period of a particular jhoom plot becomes three to 4.6 Some Historical and Recent Reasons 43 behind Such Changes four years only . However, this non-traditional jhooming, as well as growing population have lead to decline in the During 1972, the healthy vegetation scenario was quantity of healthy vegetated area, especially, in the upper observed more or less throughout the study area, despite catchment area and the area remained degraded as there of massive interference of shifting cultivation (jhum) have been complete arrests of secondary succession and

Vol 10 (22) | June 2017 | www.indjst.org Indian Journal of Science and Technology 15 Chronological Change of Land Use/Land Cover of the Muhuri River Basin from 1972 to 2016, Tripura, North-East India

favoured the weed infestation in the hilly area. Moreover, particular jhum plot, which becomes more harmful for the immigrants from Bangladesh were settled in the lower forest area of upper catchment. On the other hand, the catchment area and initially started cultivation in the low considerable quantity of forest land has been declined due lying marshy lands and gradually encroached the forested to dependency on the forest land for the firewood by the areas. Their continuous endeavour has changed the rural rural communities and increase in the percent of timber environment of this area into urbanised atmosphere. smugglers in the study area. The local people not only A large quantity of aquatic body have been altered to interfere on the unreserved forest but also create pressure agricultural land and settled area. on the reserve forest. The vegetation index of the Muhuri River basin was healthier during 2005 than the later periods. According 6. Acknowledgement to the State Forest Survey Report, 2005 such increased vegetated areas are due to the re-growth of vegetation Authors cordially acknowledge USGS for supplying in the abandoned shifting cultivation/jhum plots. In necessary satellite imagery free of cost and the local addition to that, under Joint Forest Management (JFM) people for providing necessary information during field Programme, which was active since 1991, the destruction study. of forest areas became under control of the Government till 2005 through involvement of the scheduled tribes in this programme. 7. References In today’s modern society, where the number of timber 1. Muttitanon W, Tripathi NK. Land use/cover changes in traders and smugglers has increased, there exploitation of the coastal zone of bay don bay, Thailand using landsat 5 forest resources has started exponentially in the studied TM data. International Journal of Remote Sensing. 2005; river basin. Again, according to the Forest Right Act, 26(11):2311–23. Crossref. 2006, while the State Government has started to provide 2. Gilani H, Shrestha HL, Murthy MSR, Phuntso P, Pradhan S, forest land as a ‘Patta land’ to the indigenous people for Bajracharya B, Shrestha B. Decadal land cover dynamics in Bhutan. Journal of Environmental Management, Elsevier, their better economic capability, then there is again every ScienceDirect. 2015 Jan 15; 148:91–100. Crossref. chance to use the natural forest in their own way itself. 3. Prenzel B. Remote sensing-based quantification of Moreover, the study area also becomes popular for rubber land-cover and land-use change for planning. Progress in plantation, which necessitates alteration of forest area at Planning, Elsevier, ScienceDirect. 2004 May; 61(4):281–99. a stretch. Consequently, all these factors have acted as Crossref. leading force to destroy the existing natural forest and 4. Hasan S, Mulamoottil G. Environmental problems of Dha- ka city: a study of mismanagement. Cities, Elsevier, Sci- introduction of monoculture in the Muhuri River basin enceDirect. 1994 Jun; 11(3):195–200. Crossref. of Tripura. 5. Yue Y, Wang K, Zhang B, Chen Z, Jiao Q, Liu B, Chen H. Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in karst region, South- 5. Conclusion west China. Environmental Monitoring and Assessment. 2010 Jan; 160:157–68. Crossref. The dense and open forests have been decreased 6. Shen G, Ibrahim AN, Wang Z, Ma C, Gong J. Spatial–tem- exponentially by 93.17 and 77.54 percent respectively poral land-use/land-cover dynamics and their impacts on with the passage of time. The increasing population due to surface temperature in Chongming Island of Shanghai, refugee influx from Bangladesh had created such changes China. International Journal of Remote Sensing. 2015 Aug 6; 36(15):4037–53. Crossref. significantly. As well as the settled area also increased by 7. Rogan J, Franklin J, Roberts DA. A comparison of methods 180.95 percent from the year 1972 to 2016. Moreover, the for monitoring multitemporal vegetation change using the- rubber plantation, which was totally absent during 1972, matic mapper imagery. Remote Sensing of Environment, has now occupied 24.91 percent of the total basin and Elsevier, ScienceDirect. 2002 Apr; 80(1):143–56. Crossref. is regarded as the leading factor for the decline of forest 8. Huete A, Didan K, Leeuwen WV, Miura T, Glenn E. MO- DIS vegetation indices. In Land Remote Sensing and Global cover. Although, the area under shifting cultivation has Environmental Change, Remote Sensing and Digital Image been decreased but introduction of non-traditional Processing, Springer. 2010 Aug 17; 11:579–602. jhooming has declined the period of jhum cycle over a 9. Lambin EF. Modeling and monitoring land-cover change

16 Vol 10 (22) | June 2017 | www.indjst.org Indian Journal of Science and Technology Jatan Debnath, Nibedita Das, Istak Ahmed and Moujuri Bhowmik

processes in tropical regions. Progress in Physical Geogra- to detect and monitor land use and land cover change in phy. 1997 Sep 1; 21(3):375–93. Crossref. Dhaka metropolitan of Bangladesh during 1960–2005. En- 10. Lopez E, Bocco G, Mendoza M, Duhau E. Predicting land vironmental Monitoring and Assessment, Springer. 2009 cover and land use change in the urban fringe a case in Mo- Mar; 150:237–49. Crossref relia city, Mexico. Landscape and Urban Planning, Elsevier, 25. Reed BC. Trend analysis of time-series phenology of north ScienceDirect. 2001 Aug 10; 55(4):271–85. Crossref. America derived from satellite data. GIScience and Remote 11. Vega OC, Tonini M, Conedera M, Kanveski M. Cluster rec- Sensing. 2006; 43(1):24–38. ognition in spatial-temporal sequences: the case of forest 26. Guler M, Yomralioglu T, Reis S. Using landsat data to de- fires. GeoInformatica. 2012 Oct; 16(4):653–73. Crossref. termine land use/land cover changes in Samsun, Turkey. 12. Singh A. Digital change detection techniques using remote- Environmental Monitoring and Assessment. 2007 Apr; ly sensed data. International Journal of Remote Sensing. 127(1):155–67. Crossref. 1989; 10(6):989–1003. Crossref. 27. Langley SK, Cheshire HM, Humes KS. A comparison of 13. Myers N, Mittermeier RA, Mittermeier CG, Fonseca single date and Multitemporal satellite image classifications GABD, Kent J. Biodiversity hotspots for conservation pri- in a semi-arid grassland. Journal of Arid Environments. orities. Nature. 2000 Feb 24; 403:853–8. Crossref. 2001 Oct; 49(2):401–11. Crossref. 14. Soper K. What is nature? Culture, Politics, and the non-hu- 28. Jung M, Chang E. NDVI-based land-cover change detec- man. Oxford, Blackwell; 1995 Sep 6. p. 304. tion using harmonic analysis. International Journal of Re- 15. Ponte ED, Fleckenstein M, Leinenkugel P, Parker A, Oppelt mote Sensing. 2015 Feb 20; 36(4):1097–113. N, Kuenzer C. Tropical forest covers dynamics for Latin 29. Heumann WB, Seaquist WJ, Eklundh L, Jonsson P. AVHRR America using earth observation data: a review covering derived phenological change in the Sahel and Soudan, Afri- the continental, regional, and local scale. International ca, 1982–2005. Remote Sensing of Environment. 2007 Jun Journal of Remote Sensing. 2015 Jun 30; 36(12):3196–242. 29; 108(4):385−92. Crossref. Crossref. 30. Butt MJ, Waqas A, Iqbal MF, Muhammad G, Lodhi MAK. 16. Sharma TC. The Prehistoric background of shifting culti- Assessment of urban sprawl of Islamabad metropolitan vation. Journal of Shifting Cultivation in Northeast India, area using multi-sensor and multi-temporal satellite data. Meghalaya; 1980. Arabian Journal of Science and Engineering, Springer. 2012 17. Sharma T, Kiran PVS, Singh TP, Trivedi AV, Navalgund RR. Jan; 37(1):101–14. Crossref. Hydrologic response of a watershed to land use changes: a 31. Debnath J, Das N, Ahmed I, Bhowmik M. Channel migra- remote sensing and GIS approach. International Journal of tion and its impact on land use/land cover using RS and Remote Sensing. 2001; 22(11):2095–108. Crossref. GIS: a study on Khowai river of Tripura, north-east India. 18. Miller SN, Kepner WG, Mehaffey MH, Hernandez M, Mill- The Egyptian Journal of Remote Sensing and Space Sci- er RC, Goodrich D, Devonald KK, Heggem DT, Miller WP. ence, Elsevier, ScienceDirect; 2017 Feb 6. p. 1–14. Crossref. Integrating landscape assessment and hydrologic modeling 32. Foddy GM. Status of land cover classification accuracy as- for land cover change analysis. Journal of American Water sessment. Remote Sensing of Environment, Elsevier, Sci- Resources Association. 2002 Aug; 38:915–29. Crossref. enceDirect. 2002 Apr; 80(1):185–201. Crossref. 19. Ibharim NA, Mustapha MA, Lihan T, Mazlan AG. Map- 33. Congalton RG. A review of assessing the accuracy of clas- ping mangrove changes in the Matang mangrove forest us- sifications of remotely sensed data. Remote Sensing of En- ing multi temporal satellite imageries. Ocean and Coastal vironment, Elsevier, ScienceDirect. 1991 Jul; 37(1):35–46. Management, Elsevier, ScienceDirect. 2015 Sep; 114:64–76. Crossref. Crossref 34. Singh A. Digital change detection techniques using remote- 20. Xie Y, Sha Z, Yu M. Remote sensing imagery in vegetation ly sensed data. International Journal of Remote Sensing. mapping: a review. Journal of Plant Ecology. 2008 Mar 1; 1989; 10(6):989–1003. Crossref. 1(1):9–23. Crossref. 35. Iqbal MF, Khan IA. Spatiotemporal land use land cover 21. Pasha SV, Reddy CS, Jha CS, Rao PVVP, Dadhwal VK. change analysis and erosion risk mapping of Azad Jammu Assessments of land cover change hotspots in Gulf of and Kashmir, Pakistan. The Egyptian Journal of Remote kachchh, India using multi-temporal remote sensing data Sensing and Space Science, Elsevier, ScienceDirect. 2014 and GIS. Journal of Indian Society of Remote Sensing. 2016 Dec; 17(2):209–29. Crossref. Dec; 44(6):905–13. Crossref 36. Jensen JR. Introductory digital image processing: a remote 22. Zafar SM. Spatio-temporal analysis of land cover/land use sensing perspective. 3rd edition, Prentice Hall; 2005 Jan 1. changes using geoinformatics (a case study of Margallah p. 526. hills national park). Indian Journal of Science and Technol- 37. Bouziani M, Goita K, He DC. Automatic change detection ogy. 2014 Nov; 7(11):1832–41. of buildings in urban environment from very high spatial 23. Balaji SA, Geetha P, Soman KP. Change detection of forest resolution images using existing geodatabase and prior vegetation using remote sensing and GIS techniques in Ka- knowledge. ISPRS Journal of Photogrammetry and Remote lakkad Mundanthurai tiger reserve - (a case study). Indian Sensing, Elsevier, ScienceDirect. 2010 Jan; 65(1):143–53. Journal of Science and Technology. 2016 Aug; 9(30):1–6. Crossref. 24. Dewan AM, Yamaguchi Y. Using remote sensing and GIS 38. Henits L, Jurgens C, Mucsi L. Seasonal multitemporal

Vol 10 (22) | June 2017 | www.indjst.org Indian Journal of Science and Technology 17 Chronological Change of Land Use/Land Cover of the Muhuri River Basin from 1972 to 2016, Tripura, North-East India

land-cover classification and change detection analysis of 41. Setiawan Y, Yoshino K. Change detection in land-use and Bochum, Germany, using multitemporal landsat TM data. land-cover dynamics at a regional scale from MODIS International Journal of Remote Sensing. 2016 Jan 13; time-series imagery. ISPRS Annals of Photogrammetry, 37(15):3439–54. Crossref. Remote Sensing and Spatial Information Sciences. 2012 39. Lunetta RS, Knight JF, Ediriwickrema J, Lyon JG, Worthy Aug 25 – Sep 1; 1(7):243–8. LD. Land-cover change detection using multi-temporal 42. Anderson JM, Hardy EE, Roach JT, Witmert RE. A Land MODIS NDVI data. Remote Sensing of Environment, Else- Use Classification System for Use with Remote Sensing vier, ScienceDirect. 2006 Nov 30; 105(2):142–54. Crossref. Data. U.S. Geological Survey Professional Paper, Washing- 40. Myneni RB, Hall FG, Sellers PJ, Marshak A. The Interpre- ton DC; 1976. p. 1–41. tation of spectral vegetation indexes. Institute of Electrical 43. Gupta AK. Shifting cultivation and conservation of biolog- and Electronics Engineers (IEEE) Transactions on Geosci- ic diversity in Tripura, Northeast India. Human Ecology, ence and Remote Sensing. 1995 Apr; 33(2):481–6. Springer. 2000 Dec; 28(4):605-29. Crossref.

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