[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 8, Issue 17 (2-2021) ::
PEC 2021, 8(17): 101-122 Back to browse issues page
Multi-period monitoring and prediction of forest cover loss using logistic regression model in Arasbaran catchments
Aghil Madadi * , Mortaza Gharachorlu2
Mohaghegh Ardabili University, Ardabil , aghil48madadi@yahoo.com
2- Ardabil
Abstract:   (2089 Views)
Knowledge of changes in forest cover in relation to environmental factors can be valuable in terms of conservational and protective guidances. The purpose of this study was to identify, quantify and predict deforestation in relation to topographic variables using logistic regression model. 3 Arasbaran catchments named Naposhtehchay, Ilginehchay and Mardanqumchay, located in NW in Iran  were targeted because of their vital forest ecosystems. In this regard, the reduction of forest cover as dependent variable over three different time periods including 1984-1996, 1996-2006, 2006-2017 was detected using Landsat satellite imagery and post- classification method. On the other hand, 8 independent topographic variables were derived from digital elevation model (DEM) with 30 m resolution including altitude, slope, topographic position index, topographic wetness index, north-ness, east-ness, plan curvature and profile curvature. The results of change detection over the three time periods was indicative of increasing trend of deforestation in Ilgineh and Mardanqum catchments, and ,in contrast, the declining trend of it in Naposhteh catchment. Also, logistic regression models were satisfactory and acceptable in terms of performance to explain and predict the deforestation in relation to topographic variables. The ROC values ​​obtained in the three time periods were 0.76, 0.72 and 0.64, respectively. The modeling showed that the highest probability of deforestation was attributed to convex areas and ridges, high altitudes, valleys, steep slopes, south and east aspects, and wetlands.
Keywords: Arasbaran, Deforestation, Dynamic, Temporal-Spatial variations, Topography
Full-Text [PDF 1087 kb]   (445 Downloads)    
Type of Study: Research | Subject: Special
Received: 2019/12/29 | Accepted: 2020/07/27 | Published: 2021/03/12
References
1. Agren, A.M., Lidberg, W., Stromgren, M., Oglive, J., Arp, P.A. 2014. Evaluating digital terrain indices for soil wetness mapping – a Swedish case study. Hydrology and Earth System Sciences, 11: 4103-4129.
2. Alvarenga, L. A., De Mello, C. R., Colombo, A., Cuartas, L. A., & Bowling, L. C. 2016. Assessment of land cover change on the hydrology of a Brazilian headwater watershed using the Distributed Hydrology-Soil-Vegetation Model. Catena, 143: 7-17.
3. Bax, V., Francesconi, W., & Quintero, M. 2016. Spatial modeling of deforestation processes in the Central Peruvian Amazon. Journal for Nature Conservation, 29: 79-88.
4. Bonilla-Bedoya, S., Estrella-Bastidas, A., Molina, J. R., & Herrera, M. Á. 2018. Socio-ecological system and potential deforestation in Western Amazon forest landscapes. Science of the total environment, 644: 1044-1055.
5. Detto, M., Muller-Landau, H. C., Mascaro, J., & Asner, G. P. 2013. Hydrological networks and associated topographic variation as templates for the spatial organization of tropical forest vegetation. PLoS One, 8(10), e76296.
6. Dias, L. C. P., Macedo, M. N., Costa, M. H., Coe, M. T., & Neill, C. 2015. Effects of land cover change on evapotranspiration and streamflow of small catchments in the Upper Xingu River Basin, Central Brazil. Journal of Hydrology: Regional Studies, 4: 108-122.
7. Fox, D. M., Witz, E., Blanc, V., Soulié, C., Penalver-Navarro, M., & Dervieux, A. 2012.A case study of land cover change (1950–2003) and runoff in a Mediterranean catchment.Applied Geography, 32(2): 810-821.
8. Jenness, J. 2012. Dem Surface Tools. Jenness Enterprises, Available at: http://www.jennessnet.com/arcgis/surface_area.htm.
9. Kumar, R., Nandy, S., Agarwal, R., & Kushwaha, S.P.S. 2014. Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecological Indicators, 45: 444-455.
10. Ma, J., Lin, G., Chen, J., Yang, L. 2010.An improved topographic wetness index considering topographic position. 18th International Conference on Geoinformatics, 18-20 June 2010, Beijing, pp. 1-4. DOI: 10.1109/GEOINFORMATICS.2010.5567607.
11. Reddy C.S. & Saranya K.R.L. 2017.Earth observation data for assessment of nationwide land cover and long-term deforestation in Afghanistan. Global and Planetary Change, 155:155-164.
12. Rodriguez-moreno, V.M., Bullock, S.H. 2014.Vegetation response to hydrologic and geomorphic factors in an arid region of the Baja California Peninsula. Environ Monit Assess, 186: 1009–1021.
13. Salazar, A., Baldi, G., Hirota, M., Syktus, J., & McAlpine, C. 2015. Land use and land cover change impacts on the regional climate of non-Amazonian South America: A review. Global and Planetary Change, 128: 103-119.
14. Sarma, K., & Barik, S.K. 2010. Geomorphological risk and conservation imperatives in nokrek biosphere reserve, meghalaya using geoinformatics. NeBIO, 1(2): 14-24.
15. Tovar, C., Seijmonsbergen, A. C., & Duivenvoorden, J. F. 2013. Monitoring land use and land cover change in mountain regions: An example in the Jalca grasslands of the Peruvian Andes. Landscape and Urban Planning, 112: 40-49.
16. Wei, X., Sun, G., Liu, S., Jiang, H., Zhou, G., & Dai, L. 2008. The forest‐streamflow relationship in China: a 40‐year retrospect 1. JAWRA Journal of the American Water Resources Association, 44(5): 1076-1085
17. Wilson, J.P., and Gallant, J.C. 2000. Terrain Analysis: Principles and Applications. New York, John Wiley and Sons. 479p.
18. www.googleearth.com
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


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

Madadi A, Gharachorlu M. Multi-period monitoring and prediction of forest cover loss using logistic regression model in Arasbaran catchments. PEC 2021; 8 (17) :101-122
URL: http://pec.gonbad.ac.ir/article-1-644-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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 0.08 seconds with 37 queries by YEKTAWEB 4645