Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation
AbstractDemand for agricultural food production is projected to increase dramatically in the coming decades, putting at risk our clean water supply and prospects for sustainable development. Fragmentation-free land allocation (FF-LA) aims to improve returns on ecosystem services by determining both space partitioning of a study area and choice of land-use/land-cover management practice (LMP) for each partition under a budget constraint. In the context of large-scale industrialized food production, fragmentation (e.g., tiny LMP patches) discourages the use of modern farm equipment (e.g., 10- to 20-m-wide combine harvesters) and must be avoided in the allocation. FF-LA is a computationally challenging NP-hard problem. We introduce three frameworks for land allocation planning, namely collaborative geodesign, spatial optimization and a hybrid model of the two, to help stakeholders resolve the dilemma between increasing food production capacity and improving water quality. A detailed case study is carried out at the Seven Mile Creek watershed in the midwestern US. The results show the challenges of generating near-optimal solutions through collaborative geodesign, and the potential benefits of spatial optimization in assisting the decision-making process. View Full-Text
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Xie, Y.; Runck, B.C.; Shekhar, S.; Kne, L.; Mulla, D.; Jordan, N.; Wiringa, P. Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation. ISPRS Int. J. Geo-Inf. 2017, 6, 226.
Xie Y, Runck BC, Shekhar S, Kne L, Mulla D, Jordan N, Wiringa P. Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation. ISPRS International Journal of Geo-Information. 2017; 6(7):226.Chicago/Turabian Style
Xie, Yiqun; Runck, Bryan C.; Shekhar, Shashi; Kne, Len; Mulla, David; Jordan, Nicolas; Wiringa, Peter. 2017. "Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation." ISPRS Int. J. Geo-Inf. 6, no. 7: 226.
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