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Land Cover Mapping and Its Spatial Pattern Analysis in Nepal

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Abstract

Nepal, located in a unique transition zone spanning from plains to mountains and then to the plateau, is characterized by diverse and complex land cover. Based on an object-oriented method and decision tree classifier, a land cover product covering the whole of Nepal in 2010 (hereinafter referred to as the NepalCover-2010) was produced using 30 m-resolution Landsat TM images, consisting of 8 classes at Level I and 31 classes at Level II. The accuracy of the NepalCover-2010 product at Level II was validated using samples collected from high-resolution Google Earth images. The result showed that the overall accuracy of the product was 87.17%, with a Kappa coefficient of 0.85, making it the most accurate product among similar land cover products. The product can accurately reflect the spatial patterns of land cover in Nepal. Forests are the main land cover classes, accounting for 41% of the land, followed by croplands covering about 25%. The areal proportion of paddy fields to dry farmlands was approximately two to three. Topographical and meteorological factors presented as the determining effects on the spatial patterns of land cover in Nepal. With elevation uplift from south to north, land cover classes showed a vertical zonality ordered thus: paddy fields, evergreen broadleaf forests, dry farmlands, evergreen broadleaf shrubs, evergreen needleleaf forests, grasslands, sparse vegetation, and permanent ice/snow. Land cover mapping in Nepal contributes significantly to the basic data collection in this country, and can also be of a benefit to China’s international regional economic cooperation strategy entitled “the Belt and Road Initiative”.

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Notes

  1. 1.

    The First Law of Geography, according to Tobler (1970), is “everything is related to everything else, but near things are more related than distant things.”, which is the foundation of the fundamental concepts of spatial dependence and spatial autocorrelation.

References

  • Benz UC, Hofmann P, Willhauck G et al (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogrammetry Remote Sens 58(3):239–258

    Article  Google Scholar 

  • Bhattarai K, Conway D, Yousef M (2009) Determinants of deforestation in Nepal’s central development region. J Environ Manage 91(2):471–488

    Article  Google Scholar 

  • Chen J, Chen J, Liao AP et al (2015) Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS J Photogrammetry Remote Sens 103:7–27

    Article  Google Scholar 

  • Chen J, Zhou C, Cheng W (2007) Area error analysis of vector to raster conversion of areal feature in GIS. Acta Geodaetica Cartogr Sin 36(3):344–350

    Google Scholar 

  • Chen Z, Chen J (2006) Investigation on extracting the space information of urban land-use from high spectrum resolution image of ASTER by NDBI method. Geo-Inform Sci 8(2):137–140

    Google Scholar 

  • De Fries R, Hansen M, Townshend J et al (1998) Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. Int J Remote Sens 19(16):3141–3168

    Article  Google Scholar 

  • Friedl MA, Brodley CE, Strahler AH (1999) Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Trans Geosci Remote Sens 37(2):969–977

    Article  Google Scholar 

  • Hansen M, DeFries R, Townshend JR et al (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 21(6–7):1331–1364

    Article  Google Scholar 

  • Higaki D, Karki KK, Gautam CS (2005) Soil erosion control measures on degraded sloping lands: a case study in Midlands of Nepal. Aquat Ecosyst Health Manage 8(3):243–249

    Article  Google Scholar 

  • Jia K, Li Q, Tian Y et al (2011) A review of classification methods of remote sensing imagery. Spectrosc Spectral Anal 31(10):2618–2623

    Google Scholar 

  • Kalensky Z (1998) AFRICOVER land cover database and map of Africa. Can J Remote Sens 24(3):292–297

    Article  Google Scholar 

  • Lei G, Li A, Bian J et al (2014) An practical method for automatically identifying the evergreen and deciduous characteristic of forests at mountainous areas: a case study in Mt. Gongga Region. Acta Ecol Sin 34(24):7210–7221

    Google Scholar 

  • Lei G, Li A, Bian J et al (2016) Land cover mapping in Southwestern China using the HC-MMK approach. Remote Sens 8(4):305

    Article  Google Scholar 

  • Li J, Xu H, Li X et al (2015) Vegetation information extraction of Pinus Massoniana Forest in soil erosion areas using soil-adjusted vegetation index. J Geo-inform Sci 17(9):1128–1134

    Google Scholar 

  • Liu JY, Kuang WH, Zhang ZX et al (2014) Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J Geog Sci 24(2):195–210

    Article  Google Scholar 

  • Liu W (2015) Scientific understanding of the belt and road Initiative of China and related research themes. Progress Geography 34(5):538–544

    Google Scholar 

  • Loveland T, Reed B, Brown J et al (2000) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int J Remote Sens 21(6–7):1303–1330

    Article  Google Scholar 

  • Molden D, Sharma E (2013) ICIMOD’s strategy for delivering high-quality research and achieving impact for sustainable mountain development. Mt Res Dev 33(2):179–183

    Article  Google Scholar 

  • Pandit S (2011) Forest Cover and Land Use Changes: A Study of Laljhadi Forest (Corridor). Nepal, Tribhuvan University, Central Department of Environmental Science, Kathmandu, Nepal, Far-Western Development Region

    Google Scholar 

  • Tobler WR (1970) A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46:234–240

    Article  Google Scholar 

  • Uddin K, Shrestha HL, Murthy MSR et al (2015) Development of 2010 national land cover database for the Nepal. J Environ Manage 148:82–90

    Article  Google Scholar 

  • Wang H (2004) Guide to the World States: Nepal. Social Sciences Academic Press (China), Beijing

    Google Scholar 

  • Wolfe R, Masek J, Saleous N et al (2004). LEDAPS: mapping North American disturbance from the Landsat record. In: IEEE international geoscience and remote sensing symposium

    Google Scholar 

  • Wu B, Yuan Q, Yan C et al (2014) Land cover changes of China from 2000 to 2010. Q Sci 34(4):723–731

    Google Scholar 

  • Xu H (2005) A study on information extraction of water body with the modified normalized difference water index (MNDWI). J Remote Sens 9(5):589–595

    Google Scholar 

  • Xu X, Lin Z, Xue F et al (2003) Correlation analysis between meteorological factors and the ratio of vegetation cover. Acta Ecol Sin 23(2):221–230

    Google Scholar 

  • Zhang L, Wu B, Li X et al (2014a) Classification system of China land cover for carbon budget. Acta Ecol Sin 34(24):7158–7166

    Google Scholar 

  • Zhang Z, Li A, Lei G et al (2014b) Change detection of remote sensing images based on multiscale segmentation and decision tree algorithm over mountainous area: a case study in Panxi region Sichuan Province. Acta Ecologica Sinica 34(24):7222–7232

    Google Scholar 

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Acknowledgements

This research was funded jointly by the International Cooperation Key Project of CAS (No.GJHZ201320), the International Cooperation Partner Program of Innovative Team of CAS (No.KZZD-EW-TZ-06), the Natural Science Foundation of China (No.41631180), and the National Key Research and Development Program of China (No.2016YFA0600103, 2016YFC0500201-06). We are also thankful to Zhengjian Zhang, Xi Nan, Jianbo Tan, Dong Yan, Han Xie, Shuaiqi Zhang, Yongshuai Yang, Mingjiang Sun, Li He, Pan Huang, Jie Peng, Hanxiao Zhong, Xinyao Xie, and Xiaorong Zhang for their contributions on data processing and land cover mapping. We are also grateful to researchers from Nepal TU for their work on field investigation.

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Correspondence to Ainong Li .

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Lei, G. et al. (2017). Land Cover Mapping and Its Spatial Pattern Analysis in Nepal. In: Li, A., Deng, W., Zhao, W. (eds) Land Cover Change and Its Eco-environmental Responses in Nepal. Springer Geography. Springer, Singapore. https://doi.org/10.1007/978-981-10-2890-8_2

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