Please wait a minute...
Journal of Arid Land  2019, Vol. 11 Issue (1): 29-42    DOI: 10.1007/s40333-018-0106-y
Orginal Article     
Assessing land transformation and associated degradation of the west part of Ganga River Basin using forest cover land use mapping and residual trend analysis
MATIN Shafique*(), GHOSH Sujit, D BEHERA Mukunda
Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
Download: HTML     PDF(1881KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The west part of Ganga River Basin (WGRB) has experienced continuous land transformation since the Indus Valley Civilisation shifted from the Indus basin to the Ganga basin. Particularly in the last few decades the land transformation has increased many-folds due to the changing climate and rapid increase in population. In this paper, we assessed land transformation and associated degradation in the WGRB based on the forest cover land use (FCLU) mapping and residual trend analysis (RTA). The FCLU maps for 1975 and 2010 were generated using 216 Landsat satellite images and validated using 1509 ground points. We mapped 29 forest and 18 non-forest types and estimated a total loss of 5571 km2 forest cover and expansion in settlement areas (5396 km2). Other major changes mapped include a decrease in wetlands and water bodies, while an increase in agriculture and barren lands with an overall mapping accuracy of 85.3% (kappa, 0.82) and 88.43% (kappa, 0.84) for 1975 and 2010, respectively. We also performed the RTA analysis using GIMMS-NDVI3g to identify areas of significant negative vegetative photosynthetic change as an indicator for land degradation. All the RTA models showed monotonic nature of the residual trends and resulted as moderately positive but highly significant (P<0.001). Land degradation in the form of barren land accompanied by a decline in vegetation quality and coverage was found prominent in the basin with a possibility of an accelerated rate of land degradation in future due to the rapid loss of permanent forest cover.



Key wordsland degradation      remote sensing      NDVI      GIMMS      Ganga River Basin     
Received: 10 November 2017      Published: 10 February 2019
Corresponding Authors:
Cite this article:

MATIN Shafique, GHOSH Sujit, D BEHERA Mukunda. Assessing land transformation and associated degradation of the west part of Ganga River Basin using forest cover land use mapping and residual trend analysis. Journal of Arid Land, 2019, 11(1): 29-42.

URL:

http://jal.xjegi.com/10.1007/s40333-018-0106-y     OR     http://jal.xjegi.com/Y2019/V11/I1/29

[1] Ali M, Joshi P K, Pande S, et al. 2000. Legumes in the Indo-Gangetic Plain of India. In: Johansen C, Duxbury J M, Virmani S M, et al. Legumes in rice and wheat cropping systems of the Indo-Gangetic Plain - constraints and opportunities. International Crops Research Institute for the Semi-Arid Tropics. New York: Cornell University Press, 35-70.
[2] Bai Z G, Dent D L, Olsson L, et al.2008. Proxy global assessment of land degradation. Soil Use and Management, 24(3): 223-234.
[3] Bajocco S, de Angelis A, Perini A, et al.2012. The impact of land use/land cover changes on land degradation dynamics: a Mediterranean case study. Environmental Management, 49(5): 980-989.
[4] Behera M D, Chitale V S, Shaw A, et al.2012. Wetland monitoring, serving as an index of land use change-a study in Samaspur Wetlands, Uttar Pradesh, India. Journal of the Indian Society of Remote Sensing, 40(2): 287-297.
[5] Behera M D, Patidar N, Chitale V S, et al.2014. Increase in agricultural patch contiguity over the past three decades in Ganga River Basin, India. Current Science, 107(3): 502-511.
[6] Census India.2011. The Office of the Registrar General & Census Commissioner, India Online access. [2016-11-22]. .
[7] Dardel C, Kergoat L, Hiernaux P, et al.2014. Rain-Use-Efficiency: What it tells us about the conflicting Sahel greening and Sahelian paradox. Remote Sensing, 6: 3446-3474.
[8] Dregne H E.1977. Desertification of arid lands. Economic Geography, 53(4): 322-331.
[9] Dubey S K, Pranuthi G, Tripathi S K.2012. Relationship between NDVI and rainfall relationship over India. International Journal of Water Resources and Environmental Sciences, 1(4): 102-108.
[10] Eckert S, Hüsler F, Liniger H, et al.2015. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments, 113: 16-28.
[11] EODC. 2015. ESA Climate Change Initiative Phase II Soil Moisture: Soil Moisture ECV Product User Guide (Ver. 10), D330. [2016-08-07]. .
[12] Evans J, Geerken R.2004. Discrimination between climate and human-induced dryland degradation. Journal of Arid Environments, 57(4): 535-554.
[13] Fensholt R, Langanke T, Rasmussen K, et al.2012. Greenness in semi-arid areas across the globe 1981-2007 - an Earth Observing Satellite based analysis of trends and drivers. Remote Sensing of Environment, 121: 144-158.
[14] Gabriels D, Cornelis W M. 2009. Human-induced land degradation. In: Land Use, Land Cover and Soil Sciences - Volume III: Land Use Planning, Encyclopaedia of life support system [2016-06-22]. .
[15] Gupta R K, Naresh R K, Hobbs P R, et al. 2003. Sustainability of post-green revolution agriculture: the rice-wheat cropping systems of the Indo-Gangetic Plains and China. In: Ladha J K, Hill J E, Duxbury J M, et al., Improving the Productivity and Sustainability of Rice-Wheat Systems: Issues and Impacts. Madison: American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America, Inc., 1-25.
[16] Gupta A K, Tyagi P, Sehgal V K.2011. Drought disaster challenges and mitigation in India: strategic appraisal. Current Science, 100(12): 1795-1806.
[17] Helldén U, Tottrup C.2008. Regional desertification: a global synthesis. Global and Planetary Change, 64(3-4): 169-176.
[18] Helsel D R, Hirsch R M. 2002. Statistical methods in water resources. In: Techniques of Water Resources Investigations of the United States Geological Survey, Book 4, Hydrologic Analysis and Interpretation, Chapter A3. [2002-09-22] .
[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] Hill J, Stellmes M, Udelhoven T, et al.2008. Mediterranean desertification and land degradation: mapping related land use change syndromes based on satellite observations. Global and Planetary Change, 64(3-4): 146-157.
[21] Holben B N.1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11): 1417-1434.
[22] Ibrahim Y Z, Balzter H, Kaduk J, et al.2015. Land degradation assessment using residual trend analysis of GIMMS NDVI3g, soil moisture and rainfall in Sub-Saharan West Africa from 1982 to 2012. Remote Sensing, 7(5): 5471-5494.
[23] Kumar T L, Rao K K. 2013. Studies on spatial pattern of NDVI over India and its relationship with rainfall, air temperature, soil moisture adequacy and ENSO Geofizika, 30: 1-19. [2016-07-22]. .
[24] Lamb D. 2011. Forest and land degradation in the Asia-Pacific region. In: Regreening the Bare Hills. World Forests, Vol. 8. Springer, Dordrecht, 61.
[25] Le Q B, Nkonya E, Mirzabaev A. 2016. Biomass productivity-based mapping of global land degradation hotspots. In: Nkonya E, Mirzabaev A, von Braun J. Economics of Land Degradation and Improvement - A Global Assessment for Sustainable Development. Cham: Springer, 55-84.
[26] Leemans R, Zuidema G.1995. Evaluating changes in land cover and their importance for global change. Trends in Ecology & Evolution, 10(2): 76-81.
[27] Li X B, Li R H, Li G Q, et al.2016. Human-induced vegetation degradation and response of soil nitrogen storage in typical steppes in Inner Mongolia, China. Journal of Arid Environments, 124: 80-90.
[28] Matin S, Chitale V S, Behera M D, et al.2012. Fauna data integration and species distribution modelling as two major advantages of geoinformatics-based phytobiodiversity study in today's fast changing climate. Biodiversity and Conservation, 21(5): 1229-1250.
[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] Mbow C, Brandt M, Ouedraogo I, et al.2015. What four decades of earth observation tell us about land degradation in the Sahel? Remote Sensing, 7: 4048-4067.
[31] NBSS and LUP (National Bureau of Soil Survey and Land Use Planning). 2005. Annual Report 2005. Nagpur: NBSS and LUP, 2.
[32] Reynolds J F, Smith D M S, Lambin E F, et al.2007. Global desertification: building a science for dryland development. Science, 316(5826): 847-851.
[33] Shiva V.1991. The Violence of the Green Revolution: Third World Agriculture, Ecology, and Politics. London: Zed Books, 21.
[34] Sklenicka P.2016. Classification of farmland ownership fragmentation as a cause of land degradation: A review on typology, consequences, and remedies. Land Use Policy, 57: 694-701.
[35] Suhag R. 2016. Overview of ground water in India. In: PRS Legislative Research ("PRS”) standing committee report on Water Resources examined 10 year report. New Delhi, India. [2017-04-02]. .
[36] Symeonakis E, Calvo-Cases A, Arnau-Rosalen E.2007. Land use change and land degradation in southeastern Mediterranean Spain. Environmental Management, 40(1): 80-94.
[37] UNCCD. 1994. Elaboration of an international convention to combat desertification in countries experiencing serious drought and/or desertification, particularly in Africa. [2016-07-19]. .
[38] Wang J, Rich P M, Price K P.2003. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International Journal of Remote Sensing, 24(11): 2345-2364.
[39] Wessels K J, Prince S D, Malherbe J, et al.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.
[40] Xu L, Myneni R B, Chapin III, et al.2013. Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, 3(3): 581-586.
[41] Yan K, Park T, Yan G, et al.2016. Evaluation of MODIS LAI/FPAR product collection 6. Part 1: Consistency and improvements. Remote Sensing, 8(5): 359.
[42] Zhang Y, Xiao X, Wu X, et al.2017. A global moderate resolution dataset of gross primary production of vegetation for 2000-2016. Scientific Data, 4: 170165.
[1] ZHAO Xiaohan, HAN Dianchen, LU Qi, LI Yunpeng, ZHANG Fangmin. Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index[J]. Journal of Arid Land, 2023, 15(8): 920-939.
[2] Orhan DENGİZ, İnci DEMİRAĞ TURAN. Soil quality assessment for desertification based on multi-indicators with the best-worst method in a semi-arid ecosystem[J]. Journal of Arid Land, 2023, 15(7): 779-796.
[3] M'hammed BOUALLALA, Souad NEFFAR, Lyès BRADAI, Haroun CHENCHOUNI. Do aeolian deposits and sand encroachment intensity shape patterns of vegetation diversity and plant functional traits in desert pavements?[J]. Journal of Arid Land, 2023, 15(6): 667-694.
[4] LONG Yi, JIANG Fugen, DENG Muli, WANG Tianhong, SUN Hua. Spatial-temporal changes and driving factors of eco- environmental quality in the Three-North region of China[J]. Journal of Arid Land, 2023, 15(3): 231-252.
[5] ZHAO Lili, LI Lusheng, LI Yanbin, ZHONG Huayu, ZHANG Fang, ZHU Junzhen, DING Yibo. Monitoring vegetation drought in the nine major river basins of China based on a new developed Vegetation Drought Condition Index[J]. Journal of Arid Land, 2023, 15(12): 1421-1438.
[6] SUN Liquan, GUO Huili, CHEN Ziyu, YIN Ziming, FENG Hao, WU Shufang, Kadambot H M SIDDIQUE. Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau, China[J]. Journal of Arid Land, 2023, 15(1): 34-51.
[7] XU Mengran, ZHANG Jing, LI Zhenghai, MO Yu. Attribution analysis and multi-scenario prediction of NDVI drivers in the Xilin Gol grassland, China[J]. Journal of Arid Land, 2022, 14(9): 941-961.
[8] HUANG Xiaoran, BAO Anming, GUO Hao, MENG Fanhao, ZHANG Pengfei, ZHENG Guoxiong, YU Tao, QI Peng, Vincent NZABARINDA, DU Weibing. Spatiotemporal changes of typical glaciers and their responses to climate change in Xinjiang, Northwest China[J]. Journal of Arid Land, 2022, 14(5): 502-520.
[9] YAO Kaixuan, Abudureheman HALIKE, CHEN Limei, WEI Qianqian. Spatiotemporal changes of eco-environmental quality based on remote sensing-based ecological index in the Hotan Oasis, Xinjiang[J]. Journal of Arid Land, 2022, 14(3): 262-283.
[10] WANG Jinjie, DING Jianli, GE Xiangyu, QIN Shaofeng, ZHANG Zhe. Assessment of ecological quality in Northwest China (2000-2020) using the Google Earth Engine platform: Climate factors and land use/land cover contribute to ecological quality[J]. Journal of Arid Land, 2022, 14(11): 1196-1211.
[11] MA Xiumei, ZHOU Kefa, WANG Jinlin, CUI Shichao, ZHOU Shuguang, WANG Shanshan, ZHANG Guanbin. Optimal bandwidth selection for retrieving Cu content in rock based on hyperspectral remote sensing[J]. Journal of Arid Land, 2022, 14(1): 102-114.
[12] WU Shupu, GAO Xin, LEI Jiaqiang, ZHOU Na, GUO Zengkun, SHANG Baijun. Ecological environment quality evaluation of the Sahel region in Africa based on remote sensing ecological index[J]. Journal of Arid Land, 2022, 14(1): 14-33.
[13] Laura B RODRIGUEZ, Silvia S TORRES ROBLES, Marcelo F ARTURI, Juan M ZEBERIO, Andrés C H GRAND, Néstor I GASPARRI. Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia, Argentina[J]. Journal of Arid Land, 2021, 13(9): 918-933.
[14] ZHOU Siyuan, DUAN Yufeng, ZHANG Yuxiu, GUO Jinjin. Vegetation dynamics of coal mining city in an arid desert region of Northwest China from 2000 to 2019[J]. Journal of Arid Land, 2021, 13(5): 534-547.
[15] Ayad M F AL-QURAISHI, Heman A GAZNAYEE, Mattia CRESPI. Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index[J]. Journal of Arid Land, 2021, 13(4): 413-430.