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Analyze and Predict Processes of Deforestation Using Logistic 7104 Ali Akbar Jafarzadeh et al./ Elixir Agriculture 44 (2012) 7104-7111 Available online at www.elixirpublishers.com (Elixir International Journal) Agriculture Elixir Agriculture 44 (2012) 7104-7111 Analyze and predict processes of deforestation using logistic regression and gis (a case study of northern ilam forests, ilam province, Iran) Ali Akbar Jafarzadeh 1 and Saleh Arekhi 2,* 1Forestry, University of Sari, Sari, Iran. 2Department of Forest and Rangeland, University of Ilam, Ilam, Iran. ARTICLE INFO ABSTRACT Article history: This study aims to predict spatial distribution of deforestation and detects factors influencing Received: 20 January 2012; forest degradation of Northern forests of Ilam province. For this purpose, effects of six Received in revised form: factors including distance from road and settlement areas, forest fragmentation index, 17 February 2012; elevation, slope and distance from the forest edge on the forest deforestation are studied. In + Accepted: 26 February 2012; order to evaluate the changes in forest, images related to TM1988, ETM 2001 and + ETM 2007 are processed and classified. There are two classes as, forest and non-forest in Keywords order to assess deforestation factors. The logistic regression method is used for modeling Deforestation modeling, and estimating the spatial distribution of deforestation. The results show that about 19,294 Remote sensing, ha from forest areas are deforested in the 19 years. Modeling results also indicate that more Logistic regression, deforestation occurred in the fragmented forest cover and in the areas of proximity to Zagros forests, forest/non forest edge. Furthermore, slope and distance from road and settlement areas had Ilam province, negative relationships with deforestation rates. Meanwhile, deforestation rate is decreased Ilam. with increase in elevation. Finally, a simple spatial model is presented that is able to predict the location of deforestation by using logistic regression. The validation was also tested using ROC approach which was found to be 0.96. © 2012 Elixir All rights reserved. Introduction forests have been destroyed beyond recognition Three well-known global changes are increasing carbon (http://earthtrends.wri.org). Its high deforestation rate has placed dioxide in the atmosphere, alterations in the biochemistry of the Iran among the top ten Asia and Pacific countries that global nitrogen cycle and continuing land-use/land-cover destroy forests, with economic losses estimated at 6,800 billion change(LU/LC) (Vitousek, 1994), which generates many rials (http://earthtrends.wri.org). environmental consequences globally and locally, such as the The Zagros region is located in the west of Iran running release of greenhouse gases, the loss of biodiversity and the from northwest to southeast. Total forest area is about 5.2 sedimentation of lakes and streams (Walker, 2004). In particular, million hectares. Population pressure has led to encroachments it is recognized as the major driver of the loss of biodiversity on the forestland, for agricultural and garden use, collection of and ecosystem services (Haines-Young, 2009). The effects of fuel wood, mining, human settlements, grazing, utilization of land-use changes on biodiversity may be greater than climate branches and leaves of oak trees for feeding domestic animals, change, biotic exchange, and elevated carbon dioxide etc. People have been forced to be highly dependent on these concentration at the global scale (Sala, 2000). Deforestation is degraded forests and so the forests have been reduced known one of the most important elements in LU/LC. Globally, quantitatively and qualitatively. Since 1965 natural regeneration deforestation has been occurring at an alarming rate of 13 has been severely reduced while pests and diseases have million hectares per year (FAO, 2005). increased (Fattahi, 2003). The Mediterranean area is one of the most significantly Amini et al.(2009) carried out a study on deforestation altered hotspots on Earth (Myers et al., 2000), It has been modeling and correlation between deforestation and intensively affected by human activity for millennia (Covas and physiographic parameters, man made settlements and roads Blondel, 1998; Lavorel et al., 1998; Blondel and Aronson, 1999; parameters in the Zagros forests (Armerdeh forests, Baneh, Iran) Vallejo et al., 2005). As a result, only 4.7% of its primary using remote sensing and Geographic Information System vegetation has remained unaltered (Falcucci et al., 2007). (GIS). The result of forest change detection using forest maps of Agricultural lands, evergreen woodlands and maquis habitats 1955 and 2002 showed that 4853 ha of the forest area have been that dominate the Mediterranean basin are the result of reduced and 953 ha increased in this period. The Spearman anthropogenic disturbances over centuries or even millennia correlation test and logistic regression model were used to (Blondel and Aronson, 1995; Blondel, 2006). investigate correlation between changed forests and the Although Iran has 14.4 million hectares of forestlands, it is mentioned parameters. The result showed that there is an inverse still not safeguarding its natural heritage properly. A report by, relationship between deforestation and distance from roads. the United Nations’ Food and Agriculture Organization (FAO), Minimum and maximum deforestation were at north and east does not present a hopeful scenario for the Iranian environment. aspects, respectively. The result of applying logistic regression As an example, it reports that 11.5% of the country’s northern Tele: E-mail addresses: [email protected] © 2012 Elixir All rights reserved 7105 Ali Akbar Jafarzadeh et al./ Elixir Agriculture 44 (2012) 7104-7111 model indicated that distance from road is more effective than (1) To determine and quantify forest changes that occurred in other parameters on deforestation in the study area. the Zagros forests from 1988 to 2007. Lambin (1994) and Mas et al. (2004) mention that (2) To identify and analyze the most significant explanatory deforestation models are motivated by the following potential variables that lead to forest conversion in the Zagros forests. benefits (3) To establish a predictive model based on logistic regression (1) to provide a better understanding of how driving factors and its validation govern deforestation, Materials and Methods (2) to generate future scenarios of deforestation rates, Study area (3) to predict the location of forest clearing and, The study area is situated in the province of Ilam, west of (4) to support the design of policy responses to deforestation. Iran between 33º35´ and 33º43´ latitude and between 46º17´ and According to Kaimowitz and Angels(1998), one way to 47º13´ longitude (Figure 1) and covers about, (225,593), ha. The model deforestation is to make use of empirical models. Several main species of these forests consists; Quercus brantii, Q. studies have analyzed land-use change under these approaches infectoria and Q. libani that dominant species is Q. brantii. It (Mertens and Lambin, 2000; Pontius et al., 2004; Pontius and covers a diversity of elevation, slope, population and land-use, Spencer, 2005; Rogan et al., 2008 and Schneider and Pontius, etc. Beside the undamaged natural environment in some parts, a 2001). Logistic regression performs binomial logistic regression, major part of the area has been changed by agriculture and in which the input dependent variable must be binary in nature, grazing activities (Fattahi, 2003). that is, it can have only two possible values (0 and 1). Such regression analysis is usually employed in estimating a model that describes the relationship between one or more continuous independent variable(s) to the binary dependent variable. Logistic regression analysis fits the data to a logistic curve instead of the line obtained by ordinary linear regression. In addition to the prediction, logistic regression is also a useful statistical technique that helps to understand the relation between the dependent variable (change) and independent Fig. 1: Location of study area variables (causes) (Mas et al., 2004). Land-cover maps In the particular case of deforestation, the spatial forest Multi-temporal Landsat satellite images from April 01, change is a categorically dependent variable, which results from 1988(path 167, row 37), March 20, 2001 and May 24, 2007, are the interaction of several explanatory variables. Logistic obtained from the Global Land Cover Facility regression and GIS have been demonstrated as useful tools to (http://www.landcover.org), University of Maryland. The dates analyze deforestation by many authors (Echeverria et al., 2008; of these three images are chosen to be as closely as possible in Etter et al., 2006c; Loza, 2004; Ludeke et al., 1990; McConnell the same vegetation season. The resolutions of all images are et al., 2004; Rossiter and Loza, 2008 and Van Gils and Loza, adjusted from 28.5 m * 28.5 m to 30 m * 30 m. All visible and 2006). infrared bands (except the thermal infrared band) are used for Logistic regression analysis has the advantage of taking into the purpose of classification. Remote sensing image processing account several independent explanatory variables for prediction is performed using IDRISI Andes 15.0. of a categorical variable (Van Den Eeckhaut et al., 2006). In this The 1:25,000 digital topographic maps of the national case, the dependent variable is either change or no change that cartographic Center of Iran have been used
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