[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 8, Issue 17 (2-2021) ::
PEC 2021, 8(17): 139-156 Back to browse issues page
Investigation of the Forest and Pasture Cover Changes in Arasbaran Ecosystem during 34 years, Using Remote Sensing Technique
Saber Taghipour Mr1, Mehrdad Ghodskhah daryaei Dr *, Abouzar Heidari safari kouchi Mr1
1- faculty of natural resources
guilan university, faculty of natural resources , mdaryaei9@gmail.com
Abstract:   (305 Views)
Estimating the extent of changes in forest and rangelands land cover leads to a clear understanding of the growth or decline of these natural areas and planning for effective protection of these national assets. The aim of current study was to reveal the trend of land-use changes in the Dizmar protected area of Arasbaran vegetative area, using MSS sensor of Landsat-5 for 1984, ETM+ sensor of Landsat-7 for 2000 and OLI sensors of Landsat-7 for 2015 and 2019. For this purpose, the images were classified and supervised with artificial neural network algorithms, support vector machine and maximum probability algorithm for three classes of forest, rangeland and other uses (any other land use except forest and rangeland). The results showed that the support vector machine method with higher overall accuracy than the maximum probability methods and artificial neural network has a higher efficiency in classifying forest and rangeland cover classes in the study area. Estimates showed that during 34 years, the forest cover has decreased by 135.53 square kilometers and the rangeland and other land use cover have increased by 103.19 and 32.34 square kilometers. Also, the most type of land use change during the years 1364 to 2019 with 64.15 square kilometers was related to the conversion of forest cover. The results of the present study clearly indicate the encroachment on the forest lands of the region. This in turn highlights the need for technical rehabilitation operations in this vegetation area.
Keywords: Land degradation, forest reserve, support vector machine, classification, Landsat
Full-Text [PDF 1003 kb]   (91 Downloads)    
Type of Study: Research | Subject: Special
Received: 2020/01/20 | Accepted: 2020/08/29 | Published: 2021/03/12
1. Barakat, A., Khellouk, R., El Jazouli, A., Touhami, F., Nadem, S. 2018. Monitoring of forest cover dynamics in eastern area of Béni-Mellal Province using ASTER and Sentinel-2A multispectral data. Geology, Ecology and Landscapes, 3: 203-215.
2. Costa, L., Nunes, L., Ampatzidis, Y. 2020. A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms .Computers and Electronics in Agriculture, 172: 105334.
3. Ghebrezgabher, M.G., Yang, T., Yang, X., Wang, X., Khan, M. 2016. Extracting and analyzing forest and woodland cover change in Eritrea based on landsat data using supervised classification. Egyptian Journal of Remote Sensing and Space Sciences, 19 (1): 37-47.
4. Han, JC., Huang, Y., Zhang, H., Wu, X. 2019. Characterization of elevation and land cover dependent trends of NDVI variations in the Hexi region, northwest China. Journal of Environmental Management, 232: 1037-1048.
5. Hsu, C.¬W., Chang, C.¬C., Lin,C.¬J. 2008. A Practical Guide to Support Vector Classification.Technical Report, Department of Computer Science and Information Engineering. University of National Taiwan, Taipei, 1-12.
6. Iranmanesh, Y., Sohrabi, H., Sagheb-Talebi, KH., Hosseini., S.M., Heidari Safari Kouchi, A. 2019. Biomass, Biomass Expansion Factor (BEF) and Carbon Stock for Brant's Oak (Quercus brantii Lindl.) Forests in West-Iran. Annals of Silvicultural Research, 43 (1): 2019: 15-22.
7. Islam, K., Rahman, M.F., Jashimuddin, M. 2018. Modeling land use change using Cellular Automata and Artificial Neural Network: The case of Chunati Wildlife Sanctuary, Bangladesh. Ecological Indicators, 88: 439-453.
8. Khoi, D. D., Murayama, Y., 2010. Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam. Remote Sensing, 2(5): 1249-1272.
9. Maithani, S., Jain, R.K., Arora, M.K. 2009. An artificial neural network based approach for modelling urban spatial growth. Institute of Town Planners, India, 4: 43-51.
10. Richards J., A. 2013. Remote sensing digital image analysis, fifth edition, springer, 494 pp.
11. Sehgal, S. 2012. Remotely sensed landsat image classification using neural network approaches. Engineering Research and Applications. 2(5): 043-046.
12. Testa, S., Soudani, K., Boschetti, L., Mondino, E. B. 2018. MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests. International Journal of Applied Earth Observation and Geoinformation, 64: 132-144.
13. Wan, Ji-Zh., Wang, Ch-J., Qu, H., Liu, R., Xi-Zh, Zh. 2018. Vulnerability of forest vegetation to anthropogenic climate change in China. Science of the Total Environment, 621: 1633-1641.
14. Yuqi T. 2013. Object-oriented change detection with multi-feature in urban high-resolution remote sensing imagery. Wuhan University, Wuhan, China. 162 pp.
Send email to the article author

Add your comments about this article
Your username or Email:


XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

taghipour S, ghodskhah daryaei M, heidari safari kouchi A. Investigation of the Forest and Pasture Cover Changes in Arasbaran Ecosystem during 34 years, Using Remote Sensing Technique. PEC. 2021; 8 (17) :139-156
URL: http://pec.gonbad.ac.ir/article-1-651-en.html

Volume 8, Issue 17 (2-2021) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
Persian site map - English site map - Created in 0.04 seconds with 30 queries by YEKTAWEB 4311