Spatially-explicit sensitivity analysis for land suitability evaluation
Spatially-explicit sensitivity analysis for land suitability evaluation
复制标题
土地适宜性评价的空间明确敏感性分析
DOI：
10.1016/j.apgeog.2013.08.005
复制
发表时间：
2013-12
影响因子：
4.9
通讯作者：
Hongqi Zhang
中科院分区：
文献类型：
--
作者：
Erqi Xu;Hongqi Zhang
土地适宜性评价是土地利用规划的重要环节。采用基于地理信息系统的多准则决策技术是一种灵活有效的评价方法。实施敏感性分析以验证和校准MCDM可以增强对LSE结果的理解，并有助于做出明智的规划决策。敏感性分析在MCDM应用中的主要限制是缺乏对空间维度的洞察。为了解决这个问题，本文提出了一个新的框架，将空间配置信息的敏感性分析MCDM。该框架包括土地适宜性评价和空间明确的敏感性分析。敏感性分析耦合了空间可视化和汇总指标，其中包括传统的度量（即，的平均值的绝对变化率，MACR）和一个新的空间显式度量（地球移动器的距离，EMD）。以伊犁新垦区为代表进行了研究。我们假设权重是不确定性的唯一来源，并使用一维敏感性分析。该实验表明，小麦的专家LSE结果是稳健的，但在局部区域对权重的变化相对敏感。我们的研究结果证实，MACR和EMD可以有效地识别敏感参数的基础上的各种敏感性方面。经验模态分解从空间维度探索新的信息，这与传统的敏感性分析方法不同。这种方法提供了一个合适的框架的基础上，空间显式的灵敏度分析的有效实施MCDM强大的LSE结果。
Land suitability evaluation (LSE) is an important step in land-use planning. Using multi-criteria decision-making (MCDM) techniques based on geographic information systems is a flexible and effective approach for this evaluation process. Implementation of sensitivity analysis to validate and calibrate the MCDM can enhance the understanding of the LSE results and assist in making informed planning decisions. The main limitation of sensitivity analysis in MCDM applications is a lack of insight into the spatial dimensions. To address this issue, this paper presents a new framework that incorporates the spatial configuration information from sensitivity analysis for MCDM. The framework consists of a land suitability evaluation and a spatially explicit sensitivity analysis. The sensitivity analysis couples spatial visualization and summary indicators, which include a traditional metric (i.e., the mean of the absolute change rate, MACR) and a novel spatially explicit metric (the Earth Mover's Distance, EMD). The newly reclaimed region of Yili in China was studied as the representative area. We assumed that the weights were the only source of uncertainty and used a one-dimensional sensitivity analysis. This experiment indicated that the expert LSE results for wheat are robust but relatively sensitive in local areas to changes in the weights. Our results confirm that the MACR and EMD can effectively identify sensitive parameters based on various sensitivity aspects. The EMD explores the new information from the spatial dimensions, which differs from traditional methods for sensitivity analysis. This approach provides a suitable framework based on a spatially explicit sensitivity analysis for the effective implementation of MCDM for robust LSE results.
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DOI：
10.1016/j.envsoft.2005.09.010
发表时间：
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期刊：
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影响因子：
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