|تعداد مشاهده مقاله||7,855,765|
|تعداد دریافت فایل اصل مقاله||6,019,004|
Evaluation of vegetation changes in desertification projects using remote sensing techniques in Bam, Shahdad and Garmsar regions, Iran
|Caspian Journal of Environmental Sciences|
|دوره 19، شماره 1، فروردین 2021، صفحه 47-57 اصل مقاله (1.61 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22124/cjes.2021.4306|
|Mahmood Soltaninejad1؛ Mohammad Jafari2؛ Aliakbar Noroozi3؛ Seyed Akbar Javadi4|
|1Department of Range Management, Science and Research Branch, Islamic Azad University, Tehran, Iran|
|2Department of Reclamation of Arid and Mountainous Region, Faculty of Natural Resources, University of Tehran, Tehran, Iran|
|3Faculty of Natural Resources and the Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran|
|4Department of Natural Resources and Environment, Faculty of Agricultural Sciences and Food Industries, Science and Research Branch, Islamic Azad University, Tehran, Iran|
|The face of the earth is always changing due to human activities and natural phenomena. Therefore, to optimize the management of the natural areas, knowledge of the trend, extent and estimation of land cover / use changes is considered necessary. Reviewing these changes through satellite images 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 were performed in the areas with combat desertification projects, Shahdad, Bam and Garmsar in Iran, during a 30-year period within 1987, 2002 and 2017. The Normalized Difference Vegetation Index (NDVI) and land use maps were produced using the Enhanced Thematic Mapper Plus (ETM+), Thematic Mapper (TM) and Operational Land Imager (OLI) satellite images in the three corresponding periods for the vegetation/non-vegetation, 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 method. According to the trend of changes during the periods, the results exhibited that the vegetated lands in these three areas had an increasing trend in average 31.33%, and the non-vegetated lands were turned to vegetated lands over time. In other words, they have declined by an average of 35%. Moreover, an increasing trend was found for the agricultural lands during the periods in average 4%. Eventually, the cost-effectiveness of projects implemented in the studied areas was calculated.|
|Vegetation changes؛ Remote sensing؛ Trend determination؛ Change detection|
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. 21: 193-199
Al-doski, J, Mansor, SB, Shafri, HZ 2013, Image Classification in Remote Sensing. Journal of Environment and Earth Science, 3: 141-147.
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 2000, Journal of Forest and Poplar Research, 15: 153-165 (In Persian).
Anderson, JR 1976, A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office. https://doi.org/10.3133/pp.964
Carlson, TN & Arthur, ST 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: 49-65.
Chen, X L, Zhao, HM, Li, PX & Yin, ZY 2006, Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104: 133-146.
Engelstaedtr. K, Kohefeld, I, Tegen & Harrision 2007, Control of dust emission by vegetation and topographic depression; and evalution using dust storm frequency data. Geophysical Research Letter, 30: 1294, doi:10.1029/2002GL016471, 2003.
Fadhil, AM 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, International Society for Optics and Photonics, Vol. 8762, p. 876206.
Feizizadeh, B, Azizi, H & Valizadeh, K 2007, Extracting of land uses using Landsat 7 in Malekan region, East Azarbaijan, Iran. PhD Dissertation, Islamic Azad University of Malayer, Malayer, Iran, 134 p (In Persian).
Fichera, CR, 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: 1-18.
Forests, Range and Watershed Management Organization (2017), forest policy reform in the Hyrcanian forests a contribution of the Islamic Republic of Iran to the United Nations Strategic Plan for Forests 2017-2030 (UNSPF), 126 p.
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): 024004, DOI: 10.1088/1748-9326/8/2/024004
Giriraj, A, Ullah, MI, Murthy, MR & Beierkuhnlein, C 2008, Modeling spatial and temporal forest cover change patterns (1973-2020). A case study from South Western Ghats India, (Sensors, 8). 8(10): 6132-6153; https://doi.org/10.3390/s8106132.
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, 4: 11899-11902.
Jantz, CA & Goetz, SJ 2005, Analysis of scale dependencies in an urban land‐use‐change model. International Journal of Geographical Information Science, 19: 217-241.
Khazaee, M, Hamidian, AH, Shabani, AA, Ashrafi, S, Mirjalili, SAA & 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.
Malmiran, H, 2001, Digital processing of satellite images of Tehran. Ministry of Defense Geographic Organization Publications and Armed Forces Support. 318 p. (In Persian).
Mollalo, A, Mao, L, Rashidi, P, & Glass, GE 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: 157.
Mollalo, A, Sadeghian, A, Israel, GD, 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.
Nagendra, H. and Gadgil, M.(1999) ‘Satellite imagery as a tool for monitoring species diversity: An assessment’, Journal of Applied Ecology, 36: 388-397.
Sanjari, S & 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: 6.
Sparavigna, S 2013, Study the movement of sand dunes using Google Earth and satellite images. – Journal of Range Management, 26: 121-129.
Thuiller, W, Albert, C, Araujo, MB, Berry, PM, Cabeza, M, Guisan, A & Sykes, MT 2008, Predicting global change impacts on plant species’ distributions: future challenges. Perspectives in Plant Ecology, Evolution and Systematics, 9: 137-152.
تعداد مشاهده مقاله: 1,086
تعداد دریافت فایل اصل مقاله: 1,082