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Integrating Stakeholder Preferences and GIS-Based Multicriteria Analysis to Identify Forest Landscape Restoration Priorities

A pressing question that arises during the planning of an ecological restoration process is: where to restore first? Answering this question is a complex task; it requires a multidimensional approach to consider economic constrains and the
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  Sustainability   2014 , 6  , 935-951; doi:10.3390/su6020935 sustainability ISSN 2071-1050  Article Integrating Stakeholder Preferences and GIS-Based Multicriteria Analysis to Identify Forest Landscape Restoration Priorities   David Uribe 1, *, Davide Geneletti 2 , Rafael F. del Castillo 1  and Francesco Orsi 2   1  Interdisciplinary Research Center for Regional Integrated Development, Unit Oaxaca, National Polytechnic Institute, Hornos 1003, Santa Cruz Xoxocotlán, Oaxaca 71230, Mexico; E-Mail: 2 Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano, 77, Trento 38123, Italy; E-Mails: (D.G.); (F.O.) *  Author to whom correspondence should be addressed; E-Mail:; Tel.: +52-951-517-0610.   Received: 5 April 2013; in revised form: 25 January 2014 / Accepted: 11 February 2014 /  Published: 21 February 2014 Abstract:  A pressing question that arises during the planning of an ecological restoration  process is: where to restore first? Answering this question is a complex task; it requires a multidimensional approach to consider economic constrains and the preferences of stakeholders. Being the problem of spatial nature, it may be explored effectively through Multicriteria Decision Analysis (MCDA) performed in a Geographical Information System (GIS) environment. The proposed approach is based on the definition and weighting of multiple criteria for evaluating land suitability. An MCDA-based methodology was used to identify priority areas for Forest Landscape Restoration in the Upper Mixtec region, Oaxaca (Mexico), one of the most degraded areas of Latin America. Socioeconomic and environmental criteria were selected and evaluated. The opinions of four different stakeholder groups were considered: general public, academic, Non-governmental organizations (NGOs) and governmental officers. The preferences of these groups were spatially modeled to identify their priorities. The final result was a map that identifies the most preferable sites for restoration, where resources and efforts should be concentrated. MCDA proved to be a very useful tool in collective planning, when alternative sites have to be identified and prioritized to guide the restoration work. OPEN ACCESS   Sustainability   2014 , 6 936   Keywords:  Multicriteria decision analysis; Forest Landscape Restoration; GIS; priority areas; afforestation 1. Introduction Deforestation is one of the most serious current environmental problems. During the period of 2000  –  2010, approximately 130,000 km 2  of forest have been lost around the world each year [1]. In response, the efforts to support and promote the recovery of forest ecosystems have increased [2]. One of the most comprehensive approaches to forest restoration is the Forest Landscape Restoration (FLR) [3]. FLR is defined as a process of restoration of goods, services and ecological processes that forests can provide at a broader landscape level. It fosters restoration, based on the better assets for  both people and the environment, focusing on regaining the ecological integrity and enhancing human well-being [4]. FLR provides a framework to implement large-scale and long-term restoration attempts with the incorporation of diverse dimensions within them. It is a collaborative process that involves a wide range of actors since the planning stage [5]. Important issues that arise in restoration planning are where to restore first and where we should focus the available resources and efforts. The decision should come from consensus among stakeholders to identify priorities and the most preferable sites for restoration. This is clearly related to concerns of an economic kind and the awareness that the available resources should be directed to areas and actions that are likely to provide the greatest benefits to both nature and people. However, actor  s’ opinions are often in conflict with each other, and economic and technical restrictions limit the land available for restoration planning [5]. Hence, the problem is complex, involving different components (e.g., environmental, socioeconomic) and views (e.g., local  people, experts), and has a clear spatial dimension. Dealing with similar problems requires the application of proper tools to provide robust and informed decisions. Multicriteria Decision Analysis (MCDA) encompasses a set of techniques to support decision-making  processes [6]. It provides a framework to integrate multiple opinions and evaluation criteria, to weight them according to their importance, and select the most suitable courses of action. Malczewski [6] documented the increase in use of MCDA in synergy with the capabilities of the geographical information systems (GIS) in the last two decades. This occurred in a broad range of applications, such as water management [7,8], land use planning [9], transportation infrastructure [10,11], waste disposal [12  –  14], urban planning [15], environmental planning [16,17], and forest restoration [18  –  21].   This paper presents a spatial MCDA-based method for identifying priority areas for forest landscape restoration that considers environmental and socioeconomic criteria and evaluates them according to the opinion of different participant groups, as obtained through a survey. The framework methodology was designed in the Upper Mixtec region, in Southern Mexico, one of the most degraded areas of Latin America, where the lack of environmental data and resources go together with the urgency of implementing a regional plan for restoration. The Upper Mixtec region lies on the northwest side of Oaxaca State, in South-eastern Mexico. It covers 8,100 km², corresponding to 13% of the total area of the State. Most of the information is  Sustainability   2014 , 6 937  available at the municipal level. However, some municipalities belong to more than one region. For this reason, the boundary of the study area was arbitrarily set to include all the municipalities whose area belongs for at least 50% to the region, according to the municipal and regional division available for the State of Oaxaca [22]. As a result, 124 municipalities are incorporated in the study area for a total of 11,631 km² (Figure 1). The region is characterized by high topographic diversity with canyons, hills, intermountain  plateaus, and valleys. The elevation ranges from 550 to 3,300 m. Native vegetation cover is composed of tropical dry forest, pine-oak forest, and xerophytes scrubs. However, crops and eroded areas are common throughout the entire area. Mean annual precipitation is 692 mm with rainfall being mostly concentrated in July and August with intense showers   [23,24]. Figure 1.  Study area showing existing forest in the Upper Mixtec region, Oaxaca State, Mexico. The Upper Mixtec is a region with severe socio-economic and environmental problems derived, in  part, from wrong strategies of natural resources management. There is a critical problem of water supply, degraded areas, and bare soils (Figure 2). Population is highly impoverished and dispersed [25]. Approximately 50% of the srcinal forest cover was lost with a deforestation rate of 52.7 km 2  per year,  Sustainability   2014 , 6 938  the current cover of forest falls around 6,592.47 km 2  (56.68% of study area). Although some governmental and non-governmental efforts against deforestation have been successful, resources available to combat deforestation cannot include all areas requiring restoration [26,27]. In 2005, only 26 km 2  were reforested in the whole Oaxaca State [28]. Clearly, there is an urgent need to optimize the resources available for restoration. The main restriction to implement successful restoration projects in the Upper Mixtec region has been the lack of a good strategy of distribution for available resources, economic and technical, within a great area with high socio-ecological degradation [27]. Selecting the  best areas for restoration is a difficult task due to the many factors that should be taken into account and the particular points of view of different stakeholders that participate in the process. The use of GIS-based decision-making tools to generate a map of priority areas has not been tried in this region so far.  Figure 2.  Photograph of the Upper Mixtec region, Oaxaca State, Mexico, approximately 15 km west of Nochixtlán. It shows the loss of soils caused by deforestation (photo courtesy of Rafael F. del Castillo). 2. Experimental The methodological framework was divided into three main stages, corresponding to Simon’s  decision-making model [29]: Intelligence, design, and choice (Table 1). In the intelligence stage, we sought the opinion of stakeholders to identify and evaluate socioeconomic and environmental criteria [30]. Following Eastman  et al. [31], a thematic raster layer was developed for each of the criteria based on the available data for the study region. A constraint layer was also generated that represents the areas where restoration activities are not relevant: urban, existing forest and water zones. Finally, maps of land suitability were generated [17], reflecting the  preference of the different stakeholders. In the design stage, we established a threshold based on the suitability levels to extract the most preferable sites ( i.e. , contiguous pixels with high suitability levels), which can constitute potential restoration sites, as shown by Orsi and Geneletti [32].  Sustainability   2014 , 6 939  In the choice stage, we extracted the sites with highest suitability scores by each participant. Next, they were integrated into groups of stakeholders (academic, governmental, Non-governmental organization, and public) to create different maps of priority areas. Finally, an overall map of forest landscape restoration priorities was developed by combining the maps of the four groups. Table 1.  Sequence of steps and activities performed in this study.   2.1. Identify and Evaluate Relevant Criteria Four groups of stakeholders were identified: general public (P), academic institutions (A), non-governmental organizations (NGO) and governmental officers (G). The general public included  participants with knowledge about governmental programs for restoration and inhabitants from the region (including farmers, students, craftsmen). The selection of stakeholders was made following the method described by Geneletti [33]. We elaborated a preliminary list of people with widely known work on environmental issues or professional experience in the study region, and sought advice from them to include additional suitable people. The number of final participants was determined according to Landeta [34], who pointed a minimal of seven participants per group and thirty as the maximum number, regardless possible desertions of participants or incomplete interviews. A total of 54 interviews were conducted with stakeholders belonging to the academic (9), governmental (10), NGO (9), and general public (26) groups. The interviews were conducted individually to avoid bias in judgment influenced by dissimilar opinions [35]. The process was divided in two stages. In the first stage, we conducted two-round interviews to stakeholders face-to-face or via internet or telephone to generate a set of environmental and socio-economic criteria. In the second stage, the interviews aimed at evaluating the ten most relevant criteria. Following the method applied by Orsi  et al. [21], stakeholders were asked the question: Which criteria should be considered to select a site for forest restoration? Answers from first round were clustered into individual criteria based on semantic similarity. A preliminary list of criteria with all Stage Steps and Activities Tool Intelligence  Identify and evaluate relevant criteria  (Section 2.1) Defining relevant criteria. Assign interval values of desirability. Assign values of importance Create criteria layers set: factors and constraint   (Section 2.2)  Modeling preferences  (Section 2.3) Deriving commensurate criteria layers. Weight assignment. Aggregation Interview GIS MCE Design  Design restoration options  (Section 2.4) Define threshold based on forest loss rate. Identify and eliminate unsuitability area. Create restoration options maps for each stakeholder. MCE-GIS Choice  Identification of priority areas  (Section 2.5) Priority areas by population sectors. Map of priority areas for forest landscape restoration. GIS
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