Seasonal Wilderness Identification System using Remote Sensing Techniques : Riyadh Region as a Case Study
Abstract
Seasonal wilderness areas are those which turns temporarily into vegetation after the raining season. Most Saudi families love to visit seasonal wilderness areas to spend few days outside their congested cities. The only way to find such areas which has more vegetation than others is by asking friends, relatives or finding them by accident. Some areas remain unknown until the temporal greenery disappears and they become part of the desert. This research proposes a dynamic method of identifying seasonal wilderness areas using the techniques of GIS and remote sensing. Normalized Difference Vegetation Index (NDVI) technology with a proper change detection technique is applied to classify the seasonal greenery areas with other non seasonal ones like farms or others permanent vegetated lands.
Downloads
References
G. Dilworth et al., "Defining wilderness with pictures: an exploratory study," in R. Burns & K. Robertson (compilers): Proceedings of the 2006 Northeastern Recreation Research Symposium, pp. 295-299, 2006.
C. P. Dawson and J. C. Hendee, Wilderness management: Stewardship and protection of resources and values. Fulcrum Pub, 2009.
R. F. Nash, Wilderness and the American mind. Yale University Press, 2014.
S. F. Olson, The meaning of wilderness: Essential articles and speeches. U of Minnesota Press, 1958.
F. Sotoudehnia and L. Comber, "Measuring perceived accessibility to urban green space: an integration of gis and participatory map," in 14th AGILE Conference on Geographic Information: Advancing Geoinformation Science for a Changing World, 2011.
P. B. Landres, D. R. Spildie, L. P. Queen, et al., "Gis applications to wilderness management: potential uses and limitations," 2001.
J. Banaszek, M. Gajos, D. Karkosz, O. Rahmonov, and T. Parusel, "Using gis methods to investigate urban parks within industrial regions," Polish Journal of Environmental Studies, vol. 23, no. 2, 2014.
M. Rahman et al., "Application of gis in ecotourism development: a case study in sundarbans,
bangladesh," 2010.
S. Cabuk, H. Uygucgil, A. Cabuk, and M. Inceoglu, "Using gis and rs techniques for the determination of green area priorities within the context of sea," Int. J. Civil Environ, Eng, vol. 10, no. 2, pp. 47-58, 2010.
C. Rainyn, A proposed expanded green space plan using GIS for natural areas in Palm Beach County, Florida. Florida Atlantic University, 2012.
C. Rainyn, A proposed expanded green space plan using GIS for natural areas in Palm Beach County, Florida. Florida Atlantic University, 2012.
Y. A. Twumasi and E. C. Merem, "Gis and remote sensing applications in the assessment of change within a coastal environment in the niger delta region of nigeria," International journal of environmental research and public health, vol. 3, no. 1, pp. 98-106, 2006.
Y. Liu, H. Zhang, X. Yang, Y. Wang, X. Wang, and Y. Cai, "Identifying priority areas for the conservation of ecosystem services using gis-based multicriteria evaluation," Polish Journal of Ecology, vol. 61, no. 3, pp. 415-430, 2013.
K.Weerakoon, "Suitability analysis for urban agriculture using gis and multi-criteria evaluation," International Journal of Agricultural Science and Technology, 2014.
F. E. de Sousa, E. A. Moura, and E. Marinho-Soriano, "Use of geographic information systems (gis) to identify adequate sites for cultivation of the seaweed gracilaria birdiae in rio grande do norte, northeastern brazil," Revista Brasileira de Farmacognosia, vol. 22, no. 4, pp. 868-873, 2012.
M. J. Canty, Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python. CRC Press, 2014.
M. Hussain, D. Chen, A. Cheng, H. Wei, and D. Stanley, "Change detection from remotely sensed images: From pixel-based to object-based approaches," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 80, pp. 91-106, 2013.
T. Lillesand, R. W. Kiefer, and J. Chipman, Remote sensing and image interpretation. John Wiley & Sons, 2014.
J. P. Dandois, Remote sensing of vegetation structure using computer vision. PhD thesis, UNIVERSITY OF MARYLAND, BALTIMORE COUNTY, 2014.
J. Ejikeme, J. Igbokwe, I. Ezeomedo, D. Aweh, R. Akinroye, et al., "Analysis of risks and impacts of flooding with satellite remote sensing," Journal of Environment and Earth Science, vol. 5, no. 4, pp. 1-8, 2015.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
TRANSFER OF COPYRIGHT
JISCT is pleased to undertake the publication of your contribution to Journal of Information Sciences and Computing Technologies
The copyright to this article is transferred to JISCT(including without limitation, the right to publish the work in whole or in part in any and all forms of media, now or hereafter known) effective if and when the article is accepted for publication thus granting JISCT all rights for the work so that both parties may be protected from the consequences of unauthorized use.
