Multicriterial evaluation in the definition of protected areas at the Piedade municipality, SP.

Abstract

The preservation in situ, through the establishment of legally protected areas, is one the approaches to mitigate environmental impact and protect biodiversity in the long term. The Piedade municipality shows a favorable set of conditions for consolidating protected areas (i.e. relatively low urbanization rates, the presence of forest remnants, and portions of relevant conservation units). In this context, the present study proposed the definition of priority areas for protection using Multicriteria Evaluation (MCE). The specific objectives were to identify the criteria and weights that are important for defining protected areas and to evaluate which method of MCE, whether the Weighted Linear Combination (WLC) or the Ordered Weighted Averaging (OWA) would be more appropriate. Using the Participatory Technique (PT), we defined criteria and weights. WLC and OWA made possible the identification of areas likely to become protected, but the first method produced more general and less flexible solutions. On the other hand, OWA provided a greater number of possible answers to the question of interest. Thus, showing the low-risk level, the OWA map was considered most suitable for the study proposal.

A Goal Programming Model to Guide Decision-Making Processes towards Conservation Consensuses

“…They accessed the performance of seven forest management strategies at the regional level and calculated an Overall Forest Sustainability Performance Index in each of the study areas. Sumida and Valente [14] prioritized protection areas in the Piedade municipality, Brazil, using a combined AHP-GIS method. Decisional problems regarding sustainable forestry planning have also been analyzed with MCDM methods [19][20][21][22][23][24].…”

GIS-Based Multi-Criteria and Multi-Objective Evaluation for Sustainable Land-Use Planning (Case Study: Qaleh Ganj County, Iran) “Landuse Planning Using MCE and Mola”

“…For instance, in contrast to Boolean suitability aggregation that assumes a crisp boundary and certainty, WLC and OWA allow trade-offs between criteria by weighting the normalized criteria according to their relative importance (Jiang and Eastman, 2000;Hanssen et al 2018). Several studies demonstrate the potential of the integrated approach in using GIS and MCE (WLC or OWA) for land evaluation (Akbari et al 2019;Bunruamkaew and Murayama 2012;Chivasa et al 2019;Comber et al 2010;Jamil et al 2018;Karimi et al 2020;Motlagh and Sayadi 2015;Masoodi et al 2016b;Zolekar and Bhagat 2015;Zabihi et al 2019;Sumida and Valente 2019;Shuaibu Kara 2019). These studies showed that these approaches provide more accurate results for land suitability than the Boolean classification, also in some studies, WLC was compared to the OWA aggregation method.…”

“…These studies showed that these approaches provide more accurate results for land suitability than the Boolean classification, also in some studies, WLC was compared to the OWA aggregation method. In one case, Sumida and Valente (2019) used WLC and OWA to define land suitability in the south-eastern part of São Paulo, Brazil, and according to their results WLC provided more general and less flexible solutions, but OWA provided a greater number of possible spatial planning scenarios most suitable to the researcher's goals. Other studies (Hajizadeh et al 2020;Hanssen et al 2018) have shown OWA to have a high potential for modeling complex decision problems due to a new concept in this method called order weights.…”