Evaluation of vegetation changes in desertification projects using RS-GIS techniques

Document Type : Scientific Paper

Authors

1 PhD student in Rangeland Science, Faculty of Forestry, Rangeland and Watershed, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Professor, Faculty of Natural Resources, University of Tehran, Iran

3 Associate Professor of Soil Conservation and Watershed Management Institute, Tehran, Iran

4 Associate Professor of Forestry, Rangeland and Watershed Faculty, Science and Research Branch, Islamic Azad University, Tehran, Iran

10.29252/aridbiom.2019.1617

Abstract

The earth landscape is always changing due to human activities and natural phenomena. Therefore, in order to optimize the management of the natural areas, knowledge of the trend and extent of land cover / land use changes is considered necessary, and the estimation of these changes is of great importance. Reviewing these changes through satellite images and predicting and evaluating their potential through modeling can help environmental planners and natural resource managers to make more informed decisions. In the present study, quantitative detection and evaluation of changes in vegetation was performed in the areas with combat desertification projects, Shahdad and Bam in Kerman province and Garmsar in Semnan province, during a 30-year period within 1987, 2002 and 2017. The NDVI vegetation index and land use maps were produced using the ETM + TM and OLI satellite images in the three corresponding periods for the vegetation lands/non-vegetation lands, and agricultural lands. The Kappa coefficient of 0.83 to 0.86, 0.91 to 0.92, and 0.94 to 0.95 was calculated for 1987, 2002, and 2017, respectively, and the total accuracy was between 88 % and 97 %. After providing the land use maps in different years, the monitoring of land use changes was investigated using the change detection methods. According to the trend of changes during the studied periods, our results showed that the vegetation lands in these three areas had an increasing trend, and the non-vegetation lands were turned to vegetation lands over time. Moreover, an increasing trend was found for the agricultural lands during these three periods. Finally, the cost-effectiveness of projects implemented in the studied areas was calculated and evaluated.

Keywords


[1]. Abtahi, M., Pakparvar, M. (2002). Study the trend of land use change in Kashan region using landsat images with combining bands 3, 4, 5 and Minimum Distance method in 1976 and 1998.
[2]. Al-doski, J., Mansor, S.B., Shafri, H.Z. (2013).Image Classification in Remote Sensing. Journal of Environment and Earth Science, 3(10).
[3]. Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.
[4]. Amini, S. (2006) Study the changes in forest area and preparing the map of forest level changes in Baneh region using satellite imagery of ETM and IRS from 1962 to 200 – Journal of Forest and Poplar Research 15-1: 19.
[5]. Carlson, T.N., Arthur, S.T. (2000). The impact of land use—land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global and planetary change, 25(1), 49-65.
[6]. Chen, X. L., Zhao, H. M. Li, P. X., Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote sensing of environment, 104(2), 133-146.
[7]. Fadhil, A. M. (2013). Sand dunes monitoring using remote sensing and GIS techniques for some sites in Iraq. In PIAGENG 2013: Intelligent Information, Control, and Communication Technology for Agricultural Engineering (Vol. 8762, p. 876206). International Society for Optics and Photonics.
[8]. Fezizadeh, B., Azizi, H., Valizadeh, K. (2007). Extracting of land uses usin Landsat 7 in Malekan region, East Azarbaijan, Iran. Islamic Azad University of Malayer.
[9]. Fichera, C.R., Giuseppe, M., Maurizio, P. (2012). Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. European Journal of Remote Sensing 45(1), 1-18.

[10]. Frolking, S., Milliaman, T., Seto, K., Friedl, M. (2013). A global fingerprint of macro-scale changes in urban structure from 1999 to 2009. Environmental research letter, 8(2).

[11]. Giriraj, A., Ullah, M. I., Murthy, M.R., Beierkuhnlein, C. (2008). Modeling Spatial and Temporal Forest Cover Change Patterns (1973-2020). A Case Study from South Western Ghats India. (Sensors, 8).
[12]. Hatami, M., Shafieardekani, M. (2014). The Effect of Industrialization on Land Use Changes; Evidence from Intermediate Cities of Iran. International Journal of Current Life Sciences 2014, 4, 11899–11902.
[13]. Jantz, C. A., Goetz, S. J. (2005). Analysis of scale dependencies in an urban land‐use‐change model. International Journal of Geographical Information Science, 19(2), 217-241.
[14]. Khazaee, M., Hamidian, A. H., Shabani, A. A., Ashrafi, S. Mirjalili, S.A.A., Esmaeilzadeh, E. (2016). Accumulation of heavy metals and as in liver, hair, femur, and lung of Persian jird (Meriones persicus) in Darreh Zereshk copper mine, Iran. Environmental Science and Pollution Research, 23(4), 3860-3870.
[15]. Malmiran, H. (2001). Digital Processing of Satellite Images of Tehran, – Ministry of Defense Geographic Organization publications and Armed Forces Support.
[16]. Mollalo, A., Sadeghian, A., Israel, G. D., Rashidi, P., Sofizadeh, A., Glass, G. E. (2018). Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran. Acta tropica, 188, 187-194.
[17]. Mollalo, A., Mao, L., Rashidi, P., Glass, G. E. (2019). A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States. International journal of environmental research and public health, 16(1), 157.
[18]. Nagendra, H., Gadgil, M. (1999). Satellite imagery as a tool for monitoring species diversity: An assessment’, J. Appl. Ecol. 36, 388–397.
[19]. Report of the Forests, Range and Watershed Management Organization (2017).
[20]. Sanjari, S., abd Boroumand, N. (2013). Monitoring of land use/cover changes over the past three decades using remote sensing techniques in Zarand regionid, Kerman, Iran. Journal of Remote Sensing Applications and GIS in Natural Resources Science, 4(1): 6.
[21]. Sparavigna, S. (2013). Study the movement of sand dunes using Google Earth and satellite images. – Journal Range management, 26, 121-129.
[22]. Thuiller, W. Albert, C., Araujo, M. B., Berry, P. M. Cabeza, M., Guisan, A., Sykes, M. T. (2008). Predicting global change impacts on plant species’ distributions: future challenges. Perspectives in plant ecology, evolution and systematics, 9(3-4), 137-152.