Journal of Natural Environment

Modeling the Forest Degradation Degrees of Masal Watershed NO: 12 in Guilan Province, Using Logistic Regression

Document Type : Research Paper

Authors

1 educated at guilan university

2 Associate Prof., Faculty of Natural Resources, University of Guilan, Sowme`eh Sara, I.R. Iran.

3 Assistant Prof., Forestry, Faculty of Natural Resources, University of Guilan

4 Ph.D Student, Silviculture and Forest Ecology, Faculty of Natural Resources, University of Guilan

Abstract

Planning for management of the forests in the future is not possible without having sufficient information about the forest degradation conditions in the past. The present study was conducted to model the forest degradation levels using logistic regression in the Masal watershed NO: 12. In this study, the factors of slope, altitude, geographic direction, distance from rivers, distance from the road networks, distance from residential areas, population centers and distance from barns as independent variables and different degrees of forest degradation entered into the regression model as dependent variables. Extraction of independent variables is done by using the digital maps of the area and recording the forest degradation degrees is done through terrestrial field work. To determinate the impact of each factor a hierarchical analysis method and to model the factors the logistic regression with five connects functions including: Cauchit, Negative log-log, Complementary log-log, Logit and Probit were used. The statistical tests of similarity and Wald were used to examine the significance of the model and its coefficients. The results showed that the model constructed with the Probit connection function has the appropriate capability in modeling the forest degradation. The results also showed that 20760.32 hectares of studied forests (equivalent to 83 percent of the total forest area) were degraded from very low to moderate levels. As a result, the conservation plans for 83% of the watershed forest areas and the implementation of recovery operations for 17% of the forest area (according to the forest degradation map presented in this study) have a favorable effect on improvement of the forest condition in the studied watershed.

Keywords

Azizi ghalaty, S., Rangzan, K., Taghizadeh, A., Ahmadi, S., 2015. Application of artificial neural network and ordinary least squares regression in modeling land use changes. Forest and Wood Products, 68(1): 1-16. (in Persian).
Bagheri, R., Shataee, SH., 2010. Modeling forest areas decreases, using logistic regression (case study: Chehl-Chay catchment, Golestan province). Iranian Journal of Forest, 2(3): 243-252. (in Persian).
Dewan, A.M., Yamaguchi, Y., 2009. Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960-2005. Environmental Monitoring and Assessment, 150, 237-249.
Eastman, J.R., 2002. Idrisi for windows user’s guide ver.32, Clark labs for cartography technology and geographic analysis, (Clark University), 327.
Ghajar, I., Najafi, A., Torabi, S.­A., Boston, K., 2012. Rock share estimation in forest road excavation using the Ordinal Logistic Regression (OLR) and the Analytical Hierarchy Process (AHP). – Iranian Journal of Forest and Poplar Research 20(2): 313- 323. (In Pesrian).
Heidari Safari Kouchi, A., Rostami shahraji, T., 2019. Poplar plantation and its role in carbon sequestration, Sepid Rood Press, Rasht, Iran, 180p. (In Pesrian).
Heidari Safari Kouchi, A., Rostami Shahraji, T., Iranmanesh, Y., 2015. Comparison of allometric equations to estimate the above-ground biomass of species (Case study; poplar plantations in Chaharmahal and Bakhtiari province, Iran, Caspian Journal of Environmental Sciences, 13 (3), 237-246. (in Pesrian).
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.
Jafarzadeh, A.A., Arekhi, S., 2012. Analyze and predict processes of deforestation using logestic regression and GIS (a case study of northern Ilam forest, Ilam province, Iran). Elixir Agriculture 44, 7104-7111. (in Pesrian).
Peterson, L.K., Bergen, K.M., Brown, D.G., Vashchuk, L., Blam, Y., 2009. Forested land–cover patterns and trends over changing forest management eras in the Siberian Baikal region. Forest Ecology and Management, 257, 911-922.
Phompila, Ch., Lewis, M., Ostendorf, B., Clarke, K., 2017. Forest cover changes in Lao tropical forests: physical and socio-economic factors are the most important drivers. Land, 6(23), 1-14.
Pirbavaghar, M., 2004. Investigating the variations in forest cover in relation to topographical factors and manmade regions. Case study: East forests of Guilan province. Master thesis for forestry, University of Tehran, 136 p. (In Pesrian).
Pirbavaghar, M., 2015. Deforestation modelling using logistic regression and GIS. Journal of Forest Science, 61(5), 193-199. (In Pesrian).
Ramezani, S., Naji, HR., Mahdavi, A., 2018. Impact of Climate Change and Dust Storm on Forests Ecosystems. Second International Dust Conservation Conference. Ilam University, 25-27 April, 699-703. (In Pesrian).
Ranjbar, A., Asgari, Ms., 2003. Estimation and evaluation of forest degradation process using Landsat satellite imagery and geographic information system, Geomatics conference 82, Tehran, Mapping Organization of Iran. (In Pesrian).
Saaty, T.L., 1998. Decision Support Software, Expert Choice INC, Pittsburgh, PA, 128 p.
Sumon, N., Mizoue, N., Zawhtum, N., Kajisa, T., Yoshida, S., 2012. Factors affecting deforestation and forest degradation in selevtivetly logged production forest: A case study in Myanmar. Article in Forest Ecology and Management, 267, 190-198.
Vu, Q.M., Le, Q.B., Frossard, E., Vlek, P.L.G., 2014. Socio-economic and biophysical determinants of land degradation in Vietnam: An integrated causal analysis at the national level. Land Use Policy, 36, 605-617.
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.
Journal of Natural Environment
Volume 73, Issue 1
April 2020
Pages 49-61