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Studying evidence of land degradation in the Indian Ganga River Basin—a Geoinformatics approach


Land degradation is a long-term loss of ecosystem function and productivity which takes place due to a wide variety of land processes, namely soil erosion, soil sodification, green-cover loss, and soil conditions such as soil infertility that leads to productivity loss. About 41% of the land in India is under different forms of land degradation in which a major part lies in the Indian Ganga River Basin (IGRB). In this work, we evaluated the evidence of land degradation in the IGRB by analyzing (i) the changes in the forest cover and land use (FCLU) between 1975 and 2010, (ii) forest fragmentation status for the same time period, and (iii) decline in rain-use efficiency (RUE) during 2000–2010. The FCLU-type mapping for the year 1975 and 2010 was carried out using 216 Landsat satellite scenes that derived 40 vegetation and 7 non-vegetation classes. The highest negative change (loss) was observed in the dry deciduous forest of mixed forest formation (4699.9 km2) and gregarious formation (1337.6 km2), and a major gain in settlement (5396.3 km2) and managed lands (3408.4 km2). An increase in forest fragmentation was observed in all the forest classes with the highest rise in the deciduous forest of the central basin. A consistent decline in RUE was observed highest in the South-Western semi-arid IGRB (0.02–0.15) that stretched up to the central basin. All the three analyses showed evidence of active land degradation in the form of green-cover loss, fragmented forests, and declined primary productivity with visual evidence for some of the severely degraded areas. The use of Geoinformatics to analyze land degradation using surface indicators is promising and provide possibilities of further improvements using better resolution data.

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  1. Abdul, A. M. (2014). Analysis of vegetation structure causing top-dying in mangrove forest trees in the Sundarbans in Bangladesh. American Journal of BioScience, 2(4), 135–146.

  2. Alemie, T. C. (2009). The effect of eucalyptus on crop productivity, and soil properties in the Koga watershed, Western Amhara region, Ethiopia. Degree of Masters of Professional Studies. New York: Cornell University.

  3. Bai, Z. G., Dent, D. L., Olsson, L., & Schaepman, M. E. (2008). Proxy global assessment of land degradation. Soil Use and Management, 24(3), 223–234.

  4. Bajocco, S., De Angelis, A., Perini, L., Ferrara, A., & Salvati, L. (2012). The impact of land use/land cover changes on land degradation dynamics: a Mediterranean case study. Environmental Management, 49(5), 980–989.

  5. Bargiel, D., & Herrmann, S. (2011). Multi-temporal land-cover classification of agricultural areas in two European regions with high resolution spotlight TerraSAR-X data. Remote Sensing, 3(5), 859–877.

  6. Bellard, C., Thuiller, W., Leroy, B., Genovesi, P., Bakkenes, M., & Courchamp, F. (2013). Will climate change promote future invasions? Global Change Biology., 19(12), 3740–3748.

  7. Brandão, A. O., & Souza, C. M. (2006). Mapping unofficial roads with Landsat images: a new tool to improve the monitoring of the Brazilian Amazon rainforest. International Journal of Remote Sensing., 27, 177–189.

  8. Brett, M. (2004). When is a correlation between non-independent variables “spurious”?. Oikos 105 (3):647-656.

  9. Dardel, C., Kergoat, L., Hiernaux, P., Mougin, E., Grippa, M., & Tucker, C. J. (2014). Re-greening Sahel: 30 years of remote sensing data and field observations (Mali, Niger). Remote Sensing of Environment., 140, 350–364.

  10. Dimobe, K., Ouédraogo, A., Soma, S., Goetze, D., Porembski, S., & Thiombiano, A. (2015). Identification of driving factors of land degradation and deforestation in the wildlife reserve of Bontioli (Burkina Faso, West Africa). Global Ecology and Conservation, 4, 559–571.

  11. Eckert, S., Hüsler, F., Liniger, H., & Hodel, E. (2015). Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments, 113, 16–28.

  12. FAO. (2011). Food and Agriculture Organization (The state of the world’s land and water resources for food and agriculture (SOLAW) managing systems at risk). Rome and Earthscan, London: Food and Agriculture Organization of the United Nations.

  13. Fensholt, R., & Rasmussen, K. (2013). Analysis of trends in the Sahelian―rain-use efficiency using GIMMS NDVI, RFE and GPCP rainfall data. Remote Sensing of Environment, 115, 438–451.

  14. FSI. (2015). India State of Forest Report; Forest Survey of India, Ministry of Environment and Forest. Dehradun: Govt. of India.

  15. Gibbs, H. K., & Salmon, J. M. (2015). Mapping the world’s degraded lands. Applied Geography, 57, 12–21.

  16. Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Masek, L. T. J., & Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20, 154–159.

  17. Goparaju, L., Prasad, P. R. C., & Ahmad, F. (2017). Geospatial technology perspectives for mining vis-a-vis sustainable forest ecosystem. PESD DeGruyter, 11(1), 1–20.

  18. Hakkeling, R. T. A., & Sombroek, W. G. (1990). World map of the status of human-induced soil degradation: an explanatory note. Global Assessment of Soil Degradation GLASOD Working paper and preprint, 90(7), 1-18.

  19. Higginbottom, T. P., & Symeonakis, E. (2014). Assessing land degradation and desertification using vegetation index data: current frameworks and future directions. Remote Sensing., 6(10), 9552–9575.

  20. Johnson, D. H., Shrier, B. M., O’Neal, J. S., Knutzen, J. A., Augerot, X., O’Neil, T. A., & Pearsons, T. N. (2007). Salmonid field protocols handbook: techniques for assessing status and trends in salmon and trout populations. Bethesda: American Fisheries Society.

  21. Khan, N. M., Rastoskuev, V. V., & Satoahiozawaa, Y. (2005). Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77(3), 96–109.

  22. Kundu, A., Patel, N. R., Saha, S. K., & Dutta, D. K. (2017). Desertification in western Rajasthan (India): an assessment using remote sensing derived rain-use efficiency and residual trend methods. Natural Hazards, 86(1), 297–313.

  23. Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land use and land cover change in tropical regions. Annual Review of Environment and Resources, 28, 206–241.

  24. Lasanta, T., García-Ruiz, J. M., Pérez-Rontomé, M. C., & Sancho-Marcén, C. (2000). Runoff and sediment yield in a semi-arid environment: the effect of land management after farmland abandonment. Catena, 38, 265–278.

  25. Leemans, R., & Zuidema, G. (1995). Evaluating changes in land cover and their importance for global change. Trends in ecology & evolution, 10(2), 76–81.

  26. Lewis, S. M., & Kelly, M. (2014). Mapping the potential for biofuel production on marginal lands: differences in definitions, data and models across scales. ISPRS International Journal of Geo-Information, 3(2), 430–459.

  27. Maitima, J. M., Mugatha, S. M., Reid, R. S., Gachimbi, L. N., Majule, A., Lyaruu, H., Pomery, D., Mathai, S., & Mugisha, S. (2009). The linkages between land use change, land degradation and biodiversity across East Africa. African Journal of Environmental Science and Technology, 3(10), 310–325.

  28. Maskey, S., Uhlenbrook, S., & Ojha, S. (2011). An analysis of snow cover changes in the Himalayan region using MODIS snow products and in-situ temperature data. Climatic Change, 108(1), 391–400.

  29. Matin, S., & Behera, M. D. (2017). Alarming rise in aridity in the Ganga river basin, India, in past 3.5 decades. Current Science, 112(2), 229–230.

  30. Meakin S. (1992). The Rio Earth Summit: Summary Of The United Nations Conference On Environment And Development. Article number BP-317E. Science and Technology Division, Accessed on 17 Jan 2019

  31. Matin, S., Ghosh, S., & Behera, M. D. (2018). Land transformation and associated degradation in Indian Ganga River Basin using forest cover land use mapping and residual trend analysis. Journal of Arid Land, 1–14.

  32. Mohapatra, S. N., Pani, P., & Sharma, M. (2014). Rapid urban expansion and its implications on geomorphology: a remote sensing and GIS based study. Geography Journal, ID, 361459, 1–10.

  33. Morton, D. C., DeFries, R. S., Nagol, J., Souza, C. M., Kasischke, E. S., Hurtt, G. C., & Dubayah, R. (2011). Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data. Remote Sensing of Environment, 115, 1706–1720.

  34. Mythili, G., & Goedecke, J. (2016). Economics of land degradation in India. In Economics of land degradation and improvement–a global assessment for sustainable development (pp. 431–469). Springer International Publishing.

  35. NBSS&LUP. (2005). National Bureau of Soil Survey and Land Use Planning, Annual Report 2005, Nagpur. Nagpur: NBSS&LUP.

  36. NCA. (1976). Report of the National Commission on Agriculture (pp. 427–472). New Delhi: National Commission of Agriculture; Government of India.

  37. Papanastasis, V. P. (1999). Land degradation caused by overgrazing and wildfires and management strategies to prevent and mitigate their effects. In G. Enne, C. Sanolla, & D. Peter (Eds.), Desertification in Europe: mitigation strategies, land-use planning. European Commission (pp. 187–198). Belgium: EC.

  38. Peters, M. K., Likare, S., & Kraemer, M. (2008). Effects of habitat fragmentation and degradation on flocks of African ant-following birds. Ecological Applications, 18(4), 847–858.

  39. Pickup, G., & Chewings, V. H. (1994). A grazing gradient approach to land degradation assessment in arid areas from remotely-sensed data. International Journal of Remote Sensing, 15(3), 597–617.

  40. Prăvălie, R., Patriche, C., & Bandoc, G. (2017). Quantification of land degradation sensitivity areas in Southern and Central Southeastern Europe. New results based on improving DISMED methodology with new climate data. CATENA, 158, 309–320.

  41. Pueyo, Y., Alados, C. L., & Barrantes, O. (2006). Determinants of land degradation and fragmentation in semiarid vegetation at landscape scale. Biodiversity and Conservation, 15, 939–956.

  42. Rocha, J. C., Peterson, G. D., & Biggs, R. (2015). Regime shifts in the Anthropocene: drivers, risks, and resilience. PLoS ONE, 10(8), e0134639.

  43. Roy, P. S., Kushwaha, S. P. S., Murthy, M. S. R., Roy, A., Kushwaha, D., Reddy, C. S., Behera, M. D., Mathur, V. B., Padalia, H., Saran, S., Singh, S., Jha, C. S. & Porwal, M. C. (2012) Biodiversity characterisation at landscape level: national assessment. Indian Institute of Remote Sensing, (pp. 140), ISBN 81-901418-8-0, Dehradun, India.

  44. Ruppert, J. C., Holm, A., Miehe, S., Muldavin, E., Snyman, H. A., Wesche, K., & Linstädter, A., (2012). Meta-analysis of rain-use efficiency confirms indicative value for degradation and supports non-linear response along precipitation gradients. Journal of Vegetation Science 23(6), 1035-1050.

  45. SAC. (2016). Space application centre desertification and land degradation atlas of India (based on IRS AWiFS data of 2011-13 and 2003-05), Space Applications Centre, ISRO, Ahmedabad, India, (pp. 219), Gujurat, India.

  46. Shalaby, A., & Tateishi, R. (2007). Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the North-western coastal zone of Egypt. Applied Geography, 27(1), 28–41.

  47. Shapiro, A. C., Aguilar-Amuchastegui, N., Hostert, P., & Bastin, J. F. (2016). Using fragmentation to assess degradation of forest edges in Democratic Republic of Congo. Carbon Balance and Management, 11, 11.

  48. Sobrino, J. A., & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of Remote Sensing, 21(2), 353–366.

  49. Sole’-Benet, A., Calvo, A., Cerda, A., Lazaro, R., Pini, R., & Barbero, J. (1997). Influences of microrelief patterns and plant cover on runoff related processes in badlands from Tabernas (SE Spain). Catena, 31, 23–38.

  50. Sonneveld, B. G. J. S., Keyzer, M. A., Zikhali, P., & Merbis, M. (2010). National land degradation assessment Senegal and review of global socio-economic parameters in the LADA data base, Land Degradation Assessment (LADA) project, Report for the FAO. Amsterdam: SOW-VU.

  51. Souza, C. M., & Roberts, D. A. (2005). Multitemporal analysis of degraded forests in the Southern Brazilian Amazon. Earth Interact., 9, 1–25.

  52. Symeonakis, E., Calvo-cases, A., & Arnau-rosalen, E. (2007). Land use change and land degradation in Southeastern Mediterranean Spain. Environmental Management, 40, 80–95.

  53. Tanser, F. C., & Palmer, A. R. (1999). The application of a remotely-sensed diversity index to monitor degradation patterns in a semi-arid, heterogeneous, South African landscape. Journal of arid environments, 43(4), 477–484.

  54. Tucker, C. J., Dregne, H. E., & Newcomb, W. W. (1991). Expansion and contraction of Sahara Desert from 1980 to 1990. Science, 253, 299–301.

  55. UNEP. (1987). United Nations Environmental Programme Montreal protocol on substances that deplete the ozone layer final act, New York. United: Nations.

  56. Valle-del, H. F., Blancoa, P. D., Metternicht, G. I., & Zinck, J. A. (2010). Radar remote sensing of wind-driven land degradation processes in Northeastern Patagonia. Journal of Environmental Quality, 39(1), 62–75.

  57. Vohra, B. B. (1980). A policy for land and water. Department of Environment, Government of India: New Delhi, India, 18, 64–70.

  58. Wasseige, C., & Defourny, P. (2004). Remote sensing of selective logging impact for tropical forest management. Forest Ecology and Management, 188, 161–173.

  59. Wessels, K. J., Prince, S. D., Malherbe, J., Small, J., Frost, P. E., & VanZyl, D. (2007). Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments, 68(2), 271–297.

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Data for part of the study was taken from the project entitled “Biodiversity characterization at landscape level using remote sensing and GIS” with financial assistance from National Remote Sensing Centre, Hyderabad, their co-operation is thankfully acknowledged. Authors are also thankful to the Moderate Resolution Imaging Spectroradiometer, USA, and Climate Research Unit England, for providing productivity and climate data.

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Correspondence to Shafique Matin.

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Matin, S., Behera, M.D. Studying evidence of land degradation in the Indian Ganga River Basin—a Geoinformatics approach. Environ Monit Assess 191, 803 (2019).

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  • Landsat
  • Ganga basin
  • Forest fragmentation
  • RUE
  • Land degradation