Monitoring the extent of desertification processes in western Rajasthan (India) using geo-information science
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  • 作者:Arnab Kundu ; N. R. Patel ; S. K. Saha ; Dipanwita Dutta
  • 关键词:Desertification ; Linear spectral unmixing (LSU) ; End ; member ; Bare soil ; Vegetation covers ; Landsat Thematic Mapper (TM) ; Enhanced Thematic Mapper (ETM+) ; Time series
  • 刊名:Arabian Journal of Geosciences
  • 出版年:2015
  • 出版时间:August 2015
  • 年:2015
  • 卷:8
  • 期:8
  • 页码:5727-5737
  • 全文大小:2,753 KB
  • 参考文献:Adams JB, Smith MO, Johnson PE (1986) Spectral mixture modelling: a new analysis of rock and soil types at the Viking Lander 1 site. J Geophy Res 91(B8):8098鈥?112CrossRef
    Adams JB, Smith MO, Gillespie AR (1989) Simple models for complex natural surfaces, 1st edn, A strategy for the hyperspectral era of remote sensing, Proc. IGARSS, Vancouver, pp 16鈥?1
    Adams JB, Sabol DE, Kapos V, Almeida-Filho R, Roberts DA, Smith MO, Gillespie AR (1995) Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sens of Enviorn 52:137鈥?54CrossRef
    Ali RR, Abdel Kawy WAM (2013) Land degradation risk assessment of El Fayoum depression, Egypt. Arab J Geosci 6:2767鈥?776CrossRef
    Asis AMD, Omasa K (2007) Estimation of vegetation parameter for modeling soil erosion using linear spectral mixture analysis of Landsat ETM data ISPRS. J Photogram and Remote Sens 62:309鈥?24CrossRef
    Badreldin N, Frankl A, Goossens R (2013) Assessing the spatiotemporal dynamics of vegetation cover as an indicator of desertification in Egypt using multi-temporal MODIS satellite images. Arab J Geosci. doi:10.鈥?007/鈥媠12517-013-1142-8
    Belal AA, El-Ramady HR, Mohamed ES, Saleh AM (2014) Drought risk assessment using remote sensing and GIS techniques. Arab J Geosci 7:35鈥?3CrossRef
    Boardman JW, Kruse FA, Green RO (1995) Mapping target signatures via partial unmixing of AVIRIS data, Proc. 5th Annual JPL Airborne Earth Science Workshop, Pasadena, 23rd鈥?6th January, pp. 23鈥?26
    Chiarucci AJBW, Anderson BJ, De Dominicis V (1999) Cover versus biomass as an estimate of species abundance: does it make a difference to the conclusions? J Veg Sci 10:35鈥?2CrossRef
    Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Digital change detection methods in ecosystem monitoring: a review. Inter J Rem Sens 25:1565鈥?6CrossRef
    Dutta D, Kundu A, Patel NR (2013) Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index. Geocar Int 28:192鈥?09CrossRef
    Elmore AJ, Mustard JF, Manning SJ, Lobell DB (2000) Quantifying vegetation change in semiarid environment: precision and accuracy of spectral mixture analysis and the Normalized Difference Vegetation Index. Rem Sens of Env 73:87鈥?02CrossRef
    Foody GM (1996) Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. Int J Rem Sens 17:1317鈥?340CrossRef
    Garc铆a-Haro FJ, Sommer S, Kemper T (2005) A new tool for variable multiple endmember spectral mixture analysis (VMESMA). Int J Rem Sens 26:2135鈥?162CrossRef
    Hill J, Megier J, Mehl W (1995) Land degradation, soil erosion, and desertification monitoring in Mediterranean ecosystems. Rem Sens Rev 12:107鈥?30CrossRef
    Hostert P, Roder A, Hill J (2003) Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands. Rem Sens of Env 87:183鈥?97CrossRef
    Kelarestaghi A, Jeloudar ZJ (2011) Land use/cover change and driving force analyses in parts of northern Iran using RS and GIS techniques. Arab J Geosci 4:401鈥?11CrossRef
    Kuemmerle T, R枚der A, Hill J (2006) Separating grassland and shrub vegetation by multidate pixel-adaptive spectral mixture analysis. Int J Rem Sens 27:3251鈥?271CrossRef
    Kumar U, Kerle N, Ramachandra TV (2007) Constrained linear spectral unmixing technique for regional land cover mapping using MODIS data. International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CIS2E 07), December 3 鈥?12
    Kundu A, Dutta D (2011) Monitoring desertification risk through climate change and human interference using remote sensing and GIS techniques. Int J Geomat And Geosc 2:21鈥?3
    Kundu A, Dutta D, Patel NR, Saha SK, Siddiqui AR (2014) Identify the process of environmental changes of Churu District, Rajasthan (India) using remote sensing indices. Asian J Geoinfor 14:14鈥?2
    Li HQ (2004) The desertification research process. World Forest Res 17:11鈥?4
    Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Rem Sens 25:2365鈥?407CrossRef
    McGwire K, Minor T, Fenstermaker L (2000) Hyperspectral mixture modelling for quantifying sparse vegetation cover in arid environments. Rem Sens of Env 72:360鈥?74CrossRef
    Mulligan M, Burke SM, Ramos C (2004) Climate change, land-use change and the 鈥渄esertification鈥?of Mediterranean Europe. In: Mazzoleni S, Di Pasquale G, Mulligan M, Di Martino P, Rego F (eds) Recent dynamics of Mediterranean vegetation and landscape. Wiley, New York, pp 259鈥?80
    Oki K, Oguma H, Sugita M (2002) Subpixel classification of alder trees using multitemporal Landsat Thematic Mapper imagery. Photogramm Eng and Rem Sens 68:77鈥?2
    Okin GS, Roberts DA, Murray B, Okin WJ (2001) Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Rem Sens of Env 77:212鈥?25CrossRef
    Othman AA, Al-Saady YI, Al-Khafaji AK, Gloaguen R (2014) Environmental change detection in the central part of Iraq using remote sensing data and GIS. Arab J Geosci 7:1017鈥?028CrossRef
    Roberts DA, Smith MO, Adams JB (1993) Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data. Remote Sens Environ 44:255鈥?69CrossRef
    Roberts DA, Gardner M, Church R, Ustin S, Scheer G, Green RO (1998) Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Rem Sens of Env 65:267鈥?79
    Sam L, Gahlot N, Prusty BG (2013) Estimation of dune celerity and sand flux in part of west Rajasthan Gadra area of the Thar Desert using temporal remote sensing data. Arab J Geosci. doi:10.鈥?007/鈥媠12517-013-1219-4
    Schowengerdt RA (1997) Remote sensing models and methods for image processing, 2nd edn. Academic, California, 522 p
    ;Seligman NG, Perevolotsky A (1994) Has intensive grazing by domestic livestock degraded Mediterranean Basin rangelands? In: Arianoutsou M, Groves RH (eds) Plant鈥揳nimal interactions in Mediterranean-type ecosystems. Kluwer, Berlin, pp 93鈥?04CrossRef
    Settle JJ, Drake NA (1993) Linear mixing and the estimation of ground cover proportions. Int J Rem Sens 14:1159鈥?177CrossRef
    Shoshany M, Svoray T (2002) Multidate adaptive spectral unmixing and its application to analysis of ecosystem transition along a climatic gradient. Rem Sens of Env 82:5鈥?0CrossRef
    Small C (2003) High spatial resolution spectral mixture analysis of urban reflectance. Rem Sens of Env 88:170鈥?86CrossRef
    Smith MO, Ustin SO, Adams JB, Gillespie AR (1990) Vegetation in deserts I: a regional measure of abundance from multispectral images. Rem Sens of Env 31:1鈥?6CrossRef
    Smith MO, Adams JB, Sabol DE (1994) Spectral mixture analysis鈥攏ew strategies for the analysis of multispectral data. In: J. Hill, J. M茅gier (Eds.). Imaging Spectrometry鈥攁 Tool for Environmental Observations (pp. 125鈥?43). Springer, Netherlands
    Thornes JB (1990) The interaction of erosional and vegetational dynamics in land degradation: spatial outcomes. In: Thornes JB (ed) Vegetation and erosion: processes and environments. Wiley, New York, pp 41鈥?4
    Tompkins S, Mustard JF, Pieters CM, Forsyth W (1997) Optimization of endmembers for spectral mixtures analysis. Rem Sens of Environ Model Softw 59:472鈥?89CrossRef
    Tromp M, Steenis MZ (1992) Deriving sub-pixel soi1 characteristics in northern Burkina Faso with spectral unmixing. Wageningen Agricultural University, The Netherlands
    Tsiourlis GM (1998) Evolution of biomass and productivity of grazed and ungrazed kermer oak shrubs in an insular phryganic ecosystem of Naxos, Greece. In V. P. Papanastasis, & D. Peter (Eds.), Ecological basis of livestock grazing in Mediterranean ecosystems. Proceedings of the international workshop held in Thessaloniki (Greece) on October 23鈥?5, 1997 (pp. 86鈥?9). Luxemburg: Office for Official Publications of the European Communities
    Ustin SL, Smith MO, Adams JB (1993) Remote sensing of ecological processes: a strategy for developing and testing ecological models using spectral mixture analysis. In: Ehlringer J, Field C (eds) Scaling physiological processes: leaf to globe. Academic, New York, pp 339鈥?57CrossRef
    van der Meer F (1995) Spectral unmixing of Landsat thematic mapper data. Int J Rem Sens 16:3189鈥?194CrossRef
  • 作者单位:Arnab Kundu (1)
    N. R. Patel (2)
    S. K. Saha (2)
    Dipanwita Dutta (3)

    1. K. Banerjee Centre of Atmospheric and Ocean Studies, Institute of Interdisciplinary Studies, University of Allahabad, Uttar Pradesh, India
    2. Indian Institute of Remote Sensing, Indian Space Research Organization, Dehradun, Uttarakhand, India
    3. Department of Remote Sensing and GIS, Vidyasagar University, Midnapur, West Bengal, India
  • 刊物类别:Earth and Environmental Science
  • 出版者:Springer Berlin Heidelberg
  • ISSN:1866-7538
文摘
Desertification is considered as a major worldwide environmental problem mainly caused by the climate changes and human activities during the last decades. Areas affected by desertification processes are gradually losing their level of biological quality and productivity. Among the different indicators of desertification, degradation of vegetation cover and increasing amount of bare soil have been popularly used by researchers. In India, desertification is one of the major sluggish hazards which is found in northwestern part of this country, mainly in the state of Rajasthan. The infringement of the Thar Desert has become a serious problem in the adjoining districts of Bikaner, Churu, and Nagaur. In this study, a linear spectral unmixing (LSU) method has been used for end-member fraction estimation primarily to differentiate the sand percentage and vegetation cover percentage. This linear spectral unmixing model is a widely used technique in remote sensing to estimate the fractions of several individual surface components present in an image pixel and the pure reflectance spectrum of a component which is called end-member. The LSU technique is able to monitor desertification process in terms of fractional changes in bare soil (sand) and vegetation covers. These two land features are the most crucial indicator of desertification and their long-term changes can produce expected result in identification of desertification process in an area. The long-term multispectral satellite data such as Landsat Thematic Mapper (TM) (1990, 1995, and 1999) and Enhanced Thematic Mapper (ETM+) (2003, 2009) have been used in this study. The time series analysis of fractional images of vegetation cover and bare soil has been employed for monitoring desertification processes over a long period. After analyzing the changes, some distinct patches of vegetation depletion coupled with increasing bare soil fraction were identified within the region that clearly indicates the ongoing process of desertification over there. Keywords Desertification Linear spectral unmixing (LSU) End-member Bare soil Vegetation covers Landsat Thematic Mapper (TM) Enhanced Thematic Mapper (ETM+) Time series

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