An integrated approach of remote sensing, GIS and swarm intelligence for zoning protected ecological areas

Li, Xiaoping; Lao, Chunhua; Xia, Li; Liu, Yilun; Chen, Yi-Min

doi:10.1007/s10980-011-9684-1

Cited by 40 publications

(27 citation statements)

References 36 publications

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“…In recent years there has been a significant effort to model the spatial connectivity, which is incorporated into the models through nonlinear functions of great complexity (Liu et al, 2012;Moilanen, 2007;Moilanen and Arponen, 2011;Wood and Dragicevic, 2007).…”

Section: Zoningmentioning

confidence: 99%

A critical review of multi-criteria decision making in protected areas

Castro-Pardo

Urios

2016

EARN

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ABSTRACT:Multi-criteria analysis in collaborative decision making can provide a useful tool to improve the governance in protected areas with strong conflicts between stakeholders. This paper offers an in-depth review about MCDM methods in protected areas. The analysis considers the topics Land Use, Management, Species, and Zoning and it is based in two dimensions: Methods and Participation. Topics and MCDM methods and Topics and Participation were significantly related and contrasted using a Chisquared test, respectively. We have identified two groups by topics: Zoning and Species use continuous non participative methods and Land Use and Management use discrete methods with increasing participation. KEYWORDS:Collaborative Decision-Making, Multi-criteria analysis, protected areas. Una revisión crítica de la toma de decisiones multi-criterio en áreas protegidas RESUMEN:El análisis multi-criterio para toma de decisiones colaborativa ofrece una herramienta útil para mejorar la gobernanza en áreas protegidas, con fuertes conflictos de intereses entre agentes. Este artículo ofrece una revisión en profundidad sobre métodos MCDM en áreas protegidas. El análisis considera los temas Uso de la tierra, Gestión, Especies y Zonificación, y se basa en dos dimensiones: Mé-todos y Participación. Los Temas y las Técnicas MCDM y los Temas y la Participación están significativamente correlacionados respectivamente según un test Chi-cuadrado. Hemos identificado dos grupos: Especies y Zonificación usan métodos continuos no participativos y Uso de la tierra y Gestión, utilizan métodos discretos donde la participación es creciente. PALABRAS CLAVE:Análisis multi-criterio, áreas protegidas, toma de decisiones colaborativa.

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Section: Zoningmentioning

confidence: 99%

A critical review of multi-criteria decision making in protected areas

Castro-Pardo

Urios

2016

EARN

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“…In the literature of land use optimization, context-based and suitability-related objectives were broadly indicated (Balling et al, 1999;Cao et al, 2012;Chandramouli et al, 2009;Duh & Brown, 2007;Karakostas & Economou, 2014;Liu, Lao, et al, 2012;Liu et al, 2013;Masoomi et al, 2013;Santé-Riveira et al, 2008;Stewart et al, 2004;Wang et al, 2004;Xiao et al, 2002). These functions were often slope, elevation, land price and distance-related factors (e.g.…”

Section: Formulation Of Multi-objective Land Use Optimization Problemmentioning

confidence: 99%

“…For the above reasons, solving land use optimization problem relies on the application of meta-heuristics and various researches had been assigned to adapt these algorithms with the framework of the land use optimization problem (Simulated Annealing: (Aerts & Heuvelink, 2002;Duh & Brown, 2007;Santé-Riveira et al, 2008); Tabu-Search: (Qi et al, 2008); Genetic Algorithm (GA): (Cao et al, 2011;Cao et al, 2012;Holzkämper & Seppelt, 2007;Janssen et al, 2008;Karakostas & Economou, 2014;Matthews, 2001;Stewart et al, 2004;Xiao et al, 2002;Zhang et al, 2010); Particle Swarm: (Liu, Lao, et al, 2012;Masoomi et al, 2013); Ant Colony: ; and Bee Colony (Yang et al, 2015)). Reviewing these researches show that although various algorithms adapted and examined by scholars for land use allocation, a slight part of the literature was dedicated to develop particle swarm based algorithms (PSO), and parallelizing particle swarm algorithm (PPSO) has not yet been considered.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Land Use Optimization Through Parallel Particle Swarm Algorithm: Case Study Baboldasht District of Isfahan, Iran

Sahebgharani¹

2016

JUEE

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Abstract:Land use planning seeks to divide land, the most valuable resource in the hands of planners, among different land types. During this process, various conflicting objectives are emerged which land use planners should prepare land use plans satisfying these objectives and deal with a large set of data and variable. For this reason, land use allocation is a multi-objective NP-hard optimization problem which is not solvable by the current exact methods. Therefore, solving land use optimization problem relies on the application of meta-heuristics. In this paper, a novel meta-heuristic named parallel particle swarm is developed to allocate seven land types (residential, commercial, cultural, educational, medical, sportive and green space) to Baboldasht district of Isfahan covered by 200 allocation cells with size 1000 m 2 for maximizing compactness, compatibility and suitability objective functions. Afterwards, the outputs of the new developed algorithm are compared to the outputs of genetic algorithm. The results demonstrated that the parallel particle swarm is better than genetic algorithm in terms of both solution quality (1.35%) and algorithm efficiency (63.7%). The results also showed that the outputs achieved by both algorithms are better than the current state of land use distribution. Thus, the method represented in this paper can be used as a useful tool in the hands of urban planners and decision makers, and supports the land use planning process.

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“…These advances of GIS-based ACO have been part of an integrated system, called the geographical simulation and optimization system or GeoSOS (Li et al 2011a). The system has been successfully applied to tackling site selection problems (Li et al 2009b), path-finding problems (Li et al 2009b(Li et al , 2011c, and problems of zoning design (Li et al 2011b(Li et al , 2012a. The ACO methods have also been used for solving the problem of searching for the best location of public facilities such as emergency services (Liu et al 2005) and fire stations (Liu et al 2006;Huang et al 2006a).…”

Section: Ant Colony Optimizationmentioning

confidence: 99%