Zoning farmland protection under spatial constraints by integrating remote sensing, GIS and artificial immune systems

Currently, with rapid expanding of urban area, the rate of conversion of agricultural land to nonagricultural uses in China is increasing. Zoning farmland protection is an important measure to protect limited land resource. This article presented an innovative approach based on the integrated use of remote sensing, GIS, and artificial immune systems (AIS) for generating farmland protection areas. Some modifications have been made for conventional AIS so that it can be further extended to the solution of zoning problems. The optimal objective is to generate farmland protection areas that minimize development potential and maximize agricultural suitability and spatial compactness. First, utility function by addressing the criteria of farmland protection is incorporated into AIS algorithm. Second, encoding and mutation of antibodies is modified so that it can be suited to the solution of spatial optimization problems. The AIS-based zoning model was then applied to a case study in Guangzhou, Guangdong, China. The experiments have demonstrated that the proposed method was an efficient and effective spatial optimization technique, which took only about 194 seconds to generate satisfied farmland protection patterns. Furthermore, the AIS-based zoning model can explore various alternatives conveniently, and it can yield better performances than nonprotection scenario in the utility efficiency of land resources and the site condition for farmland.

[1]  Hugues Bersini,et al.  The Immune Recruitment Mechanism: A Selective Evolutionary Strategy , 1991, ICGA.

[2]  A. Yeh,et al.  Economic Development and Agricultural Land Loss in the Pearl River Delta, China , 1999 .

[3]  Jerne Nk.  Towards a network theory of the immune system. , 1974 .

[4]  H.,et al.  The Immune System as a Model for Pattern Recognition and Classification , 1999 .

[5]  Jon Timmis.  On Parameter Adjustment of the Immune Inspired Machine Learning Algorithm AINE , 2000 .

[6]  Alex Alves Freitas,et al.  AISEC: an artificial immune system for e-mail classification , 2003, IEEE Congress on Evolutionary Computation.

[7]  Dipankar Dasgupta,et al.  Tool Breakage Detection in Milling Operations using a Negative-Selection Algorithm , 1995 .

[8]  B. Gardner,et al.  The Economics of Agricultural Land Preservation , 1977 .

[9]  D. Dasgupta,et al.  A formal model of an artificial immune system. , 2000, Bio Systems.

[10]  Thomas L. Saaty,et al.  Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation , 1990 .

[11]  Xiaoping Liu,et al.  Embedding sustainable development strategies in agent‐based models for use as a planning tool , 2008, Int. J. Geogr. Inf. Sci..

[12]  Elizabeth Brabec,et al.  Agricultural land fragmentation: the spatial effects of three land protection strategies in the eastern United States , 2002 .

[13]  R. Sugumaran,et al.  Development of an agricultural land evaluation and site assessment (LESA) decision support tool using remote sensing and geographic information system , 2005 .

[14]  Peter Nijkamp,et al.  Multicriteria analysis for land-use management , 1998 .

[15]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[16]  Erik Lichtenberg,et al.  Assessing farmland protection policy in China , 2008 .

[17]  G. J. Carsjens,et al.  Strategic land use allocation : dealing with spatial relationships and fragmentation of agriculture , 2002 .

[18]  Xiaohu Zhang,et al.  Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata , 2010, Int. J. Geogr. Inf. Sci..

[19]  Liangpei Zhang,et al.  A Supervised Artificial Immune Classifier for Remote-Sensing Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Xiubing Li,et al.  Cultivated land and food supply in China. , 2000 .

[21]  F. Steiner,et al.  Land suitability analysis for the upper Gila River watershed. , 2000 .

[22]  David L. Tulloch,et al.  Integrating GIS into farmland preservation policy and decision making , 2003 .

[23]  J. Chun,et al.  Shape optimization of electromagnetic devices using immune algorithm , 1997 .

[24]  Kerim Guney,et al.  A CLONAL SELECTION ALGORITHM FOR ARRAY PATTERN NULLING BY CONTROLLING THE POSITIONS OF SELECTED ELEMENTS , 2008 .

[25]  Roger L. King,et al.  An artificial immune system model for intelligent agents , 2001, Future Gener. Comput. Syst..

[26]  C. Woodcock,et al.  Monitoring land-use change in the Pearl River Delta using Landsat TM , 2002 .

[27]  Frank W. Davis,et al.  Prioritizing farmland preservation cost-effectively for multiple objectives , 2006 .

[28]  Tom Daniels,et al.  Holding Our Ground: Protecting America's Farms And Farmland , 1997 .

[29]  Dipankar Dasgupta,et al.  Parallel Search for Multi-Modal FunctionOptimization with Diversity and Learningof Immune Algorithm , 1999 .

[30]  J. Ronald Eastman,et al.  Multi-criteria and multi-objective decision making for land allocation using GIS , 1998 .

[31]  Eastman J. Ronald,et al.  RASTER PROCEDURES FOR MULTI-CRITERIA/MULTI-OBJECTIVE DECISIONS , 1995 .

[32]  G. P. Brown.  Arable Land Loss in Rural China: Policy and Implementation in Jiangsu Province , 1995 .

[33]  Anthony Gar-On Yeh,et al.  Zoning land for agricultural protection by the integration of remote sensing, GIS, and cellular automata , 2001 .

[34]  A. Yeh,et al.  Economic Development and Agricultural Land Loss in the Pearl River Delta , 1997 .

[35]  Y. Ying,et al.  External benefits of preserving agricultural land: Taiwan's rice fields , 2005 .

[36]  N K Jerne,et al.  Towards a network theory of the immune system. , 1973, Annales d'immunologie.

[37]  D. Levia.  Farmland conversion and residential development in North Central Massachusetts , 1998 .

[38]  Fulong Wu,et al.  Calibration of stochastic cellular automata: the application to rural-urban land conversions , 2002, Int. J. Geogr. Inf. Sci..