Landscape Change in the Steppe of Algeria South-West Using Remote Sensing

Open access


Landscape dynamics is the result of interactions between social systems and the environment, these systems evolving significantly over time. climatic conditions and biophysical phenomena are the main factors of landscape dynamics. Also, currently man is responsible for most changes affecting natural ecosystems. The objective of this work is to study the dynamics of a typical landscape of western Algeria in time and space, and to map the distribution of vegetation groups constitute the vegetation cover of this ecosystem. as well as using a method of monitoring the state of a fragile ecosystem by remote sensing to understand the processes of changes in this area. The steppe constitutes a large arid area, with little relief, covered with low and sparse vegetation. it lies between the annual isohyets of 100 to 400 mm, subjected to a very old human exploitation with an activity of extensive breeding of sheep, goats, and camels. Landsat satellite data were used to mapping vegetation groups in the Mecheria Steppe at a scale of 1: 300,000. Then, a comparison was made between the two maps obtained by a classification of Landsat-8 sensor Operational Land Imager (OLI) acquired on March 18, 2014, and Landsat-5 sensor Thematic Mapper (TM) acquired on April 25, 1987. The results obtained show the main changes affecting the natural distribution of steppe species, a strong change in land occupied by the Stipa tenacissima steppe with 65% of change, this steppe is replaced by Thymelaea microphylla, Salsola vermiculata, lygeum spartum and Peganum harmala steppe. an absence from the steppe Artemisia herba-alba that has also been replaced by the same previous steppes species. The groups with Quercus ilex and Juniperus phoenicea are characterized by a strong regression that was lost 60% of its global surface and transformed by steppe to stipa tenacissima and bare soil.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Aidoud A. (1994). Pâturage et désertification des steppes arides en Algérie. Cas de la steppe d'alfa (Stipa tenacissima L.). Paralelo 37° 16 : 33-42. (in French)

  • Alphan H. (2003). Land use change and urbanization in Adana Turkey. Land Degradation Development; 14(6): 575-86.

  • Ancona N. Maglietta R. Stella E. (2006). Pattern Recognition. 39: 1588.

  • Benabadji N. & Bouazza M. (2000). Quelques Modifications Climatiques Intervenues dans le Sud- Ouest de l’Oranie (Algérie Occidentale). Energ. Ren. 3 117-125. (in French)

  • Bernstein LS. Adler-Golden SM. Sundberg RL. Ratkowski A. (2006). Improved Reflectance Retrieval from Hyper- and Multispectral Imagery without Prior Scene or SensorInformation. In: SPIE Proceedings Remote Sensing ofClouds and the Atmosphere XI; vol. 6362.

  • Bernstein LS. Adler-Golden SM. Sundberg RL. et al. (2005). Validation of the Quick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery. In: SPIE Proceedings Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery XI; 5806: 668-678.

  • Chavez PS. (1996). Image-based atmospheric corrections revisited and improved. Photogrammetr Eng Remote Sensing; 62: 1025-36.

  • Chen Xiuwan. (2002). Using remote sensing and GIS to analyze land cover change and impacts on regional sustainable development. International Journal of Remote Sensing 23(1):107-125

  • Chi M. Feng R. Bruzzone L. (2008). Advances in Space Research; 41: 1793.

  • Coppin P. Jonckheere I. Nackaerts K. Muys B. Lambin E. (2004). Digital change detection methods in ecosystem monitoring: a review. Int. J. Remote Sens; 25: 1565-96.

  • Congalton R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37 35-46.

  • Dai XL. &Khorram S. (1999). Remotely sensed change detection based on artificial neural networks. Photogrammetr Eng Remote Sensing; 65(10): 1187-94.

  • Dewan AM. & Yamaguchi Y. (2009). Land use and land cover change in greater Dhaka Bangladesh: using remote sensing to promote sustainable urbanization. ApplGeograp; 29(4): 390-401.

  • Dube T. Mutanga O. (2015). Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment South Africa.

  • ISPRS J. Photogramm. Remote Sens. 101: 36-46.

  • Güler M Yomralio_glu T Reis S. (2007). Using Landsat data todetermine land use/land cover changes in Samsun Turkey. Environ Monitor Assess; 127: 155-67.

  • Knorn J. Rabe A. Radeloff V.C. Kuemmerle T. Kozak J. and Hostert P. (2009) ‘Land cover mapping of large areas using chain classification of neighboring Landsat satellite images’ Remote Sensing of Environment vol. 113 no. 5 pp. 957-964.

  • Haddouche I. (2009). La télédétection et la dynamique de paysage en milieu arid et semi-arid en Algérie. Cas de la région de Naama. Phd thesis University of Tlemcen. (in French)

  • HCDS. (2014). Problématique des zones steppiques et perspectives de développement. Rap. Synth. haut commissariat au développement de la steppe 10 p. (in French)

  • Hirche A. Boughani A. Salamani M. (2007). Évolution de la pluviosité annuelle dans quelques stations arides algériennes. Sécheresse 18(4) 314-320.(in French)

  • Howarth PJ. &Wickware GM. (1981). Procedures for change detection using Landsat digital data. Int J Remote Sensing; 2(3): 277-91.

  • Huang C. Goward S. Schleeweis K. Thomas N. Masek J. Zhu Z. (2009). Dynamics of national forests assessed using the Landsat record: Case studies in eastern United States. Remote Sensing of Environ; 113: 1430-42.

  • Huang C. Davis L. Townshend J. (2002). An assessment of Support Vector Machines for land cover classification. Int J Remote Sensing; 23: 725-49.

  • Immitzer M. Atzberger C. Koukal T. (2012). Tree species classification with random Forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens. 4:2661-2693.

  • Irons J.R. Dwyer J.L. Barsi J.A. (2012). The next Landsat satellite: the Landsat data continuity mission. Remote Sens. Environ. 122:11-21. 2011.08.026.

  • Janz A. vander-Linden S. Waske B. Hostert P. (2007). Image SVM A user-oriented tool for advanced classification of hyperspectral data using Support Vector Machines. Proceedings 5th EARSeL Workshop on Imaging Spectroscopy. Bruges Belgium April 23-25

  • Jensen JR. (2004). Digital change detection. Introductory digital image processing: A remote sensing perspective. New Jersey’ Prentice-Hall; pp. 467-494.

  • Jensen JR. (1996). Introductory digital image processing: A remote sensing perspective. Prentice Hall.

  • Julien Y Sobrino JA Jiménez-Mu_noz J-C. (2011). Land use classification from multitemporal Landsat imagery using Yearly Land Cover Dynamics (YLCD) method. Int J Appl Earth ObserGeoinform; 13: 711-20.

  • Karlson M. Ostwald M. Reese H. Sanou J. Tankoano B. Mattsson E. (2015). Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sens. 7:10017-10041.

  • Khaldoun A. (2000). Évolution technologique et pastoralisme dans la steppe algérienne. Le cas du camion Gak en hautes-plaines occidentales. Options Médi. 39: 121-127. (in French)

  • Le Houerou HN. (1985). La régénération des steppes algérienne. IDOVI Institut national agronomique Alger. (in French)

  • Micheletti N. Kanevski M. Bai SB. Wang J. Hong T. (2011). Intelligent analysis of landslide data using machine learning algorithms. Proceedings of the Second World Landslide Forum 3-7 October; Rome.

  • Mundia C. & Aniya M. (2006). Dynamics of land use/ cover changes and degradation of Nairobi City Kenya. Land Degradation Development; 17(1): 97-108.

  • Mutanga O. Adam E. Cho M.A. (2012). High-density biomass estimation for wetland vegetation using Worldview-2 imagery and random forest regression algorithm. Int. J. Appl. Earth Obs. Geoinf. 18:399-406.

  • Nedjimi B. Sebti M. Naoui T. H. (2008). Le problème du foncier agricole en Algérie. Revue Droit Sci. Hum. 1: 1-11. (in French)

  • Nedjraoui D. & Bédrani S. (2008). La désertification dans les steppes algériennes : causes impacts et actions de lutte. VertigO - la revue électronique en sciences de l'environnement 8(1) (in French)

  • Petropoulos G. Kontoes C. Keramitsoglou I. (2010). Burnt area delineation from a uni-temporal perspective based onLandsat TM imagery classification using support vector machines. Int JAppl Earth ObserGeoinform; 13: 70-80.

  • Quézel P. & Barbéro M. (1993). Climatic variations in the Sahara and in dry Africa since the Pliocene: lessons of the current flora and vegetation. Bull. Ecol. 24: 191-202.

  • Quézel P. & Barbéro M. (1990). Mediterranean forests: problems posed by their historical ecological significance and their conservation. Acta Botanica Malacitana. 15: 145-178.

  • Quezel P. & Santa S. (1962). New flora of Algeria andsouthern desert regions. C.N.R.S. Paris. 2 vols. 1170p. (in French)

  • Ridd MK. &Liu J. A. (1998). comparison of four algorithms for changedetection in an urban environment. Remote Sens Environ; 63: 95-100.

  • Sader S.A. Hayes D.J. Hepinstall J.A. Coan M. Soza C. (2001). Forest change monitoring of a remote biosphere reserve. International Journal of Remote Sensing 22 1937-1950.

  • Scholkopf B. Smola A. (2002). Learning with Kernels MIT Press Cambridge Mass.

  • Schott J. Salvaggio C. Volchok W. (1988). Radiometric scenenormalization using pseudoinvariant features. Remote Sensing Environ; 26: 1-16.

  • Singh A. (1989). Digital change detection techniques using remotelysensed data. Int J Remote Sens; 10: 989-1003.

  • Siren AH. & Brondizio ES. (2009). Detecting subtle land use change in tropical forests. ApplGeograp; 29(2): 201-11.

  • Sitayeb T. & Benabdeli K. (2008). Contribution to the study of land-use dynamics in the plains of Macta (Algeria) with the aid of remote sensing and GIS. C R Biol. Vol. 331 no. 6 pp. 466-474. doi:

    • Crossref
    • Export Citation
  • Yilmaz I. (2010). Comparison of landslide susceptibility mapping methodologies for Koyulhisar Turkey: conditional probability logistic regression artificial neural networks and support vector machine. Environ Earth Sci; 61(4): 821-36.

  • Yuan D. Elvidge CD. Lunetta RS. (1998). Survey of multispectral methods for land cover change analysis. In: Lunetta RS Elvidge C (eds) Remote sensing change detection: environmental monitoring methods and applications. Taylor & Francis London; pp. 21-39.

  • Yuan F. Sawaya KE. LoeffelholzBC. Bauer ME. (2005). Land cover classification and change analysis of the Twin Cities(Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sens Environ; 98: 317-28.

  • Vapnik V.N. (2000). The Nature of Statistical Learning Theory Springer NY.

  • Werle Dirk Timothy C. Martin and Khaled Hasan. 2000. “Flood and Coastal Zone Monitoring in Bangladesh with Radarsat ScanSAR: Technical Experience and Institutional Challenges.” Johns Hopkins APL Technical Digest (Applied Physics Laboratory) 21(1): 148-54.

Journal information
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 282 101 3
PDF Downloads 153 82 4