基于变化向量的耕地利用方式变化下耕地质量评价

耕地利用方式变化下耕地质量的变化研究意义重大。该文以浙江省耕地质量监测试点松阳县为例,以变化向量凸显耕地利用方式变化,探讨基于变化向量的耕地利用方式变化下耕地质量变化的快速评价方法。充分考虑各特征向量之间的联系以及不同意义不同量纲元素间的共同作用问题,选择典型相关分析CCA(canonical correlation analysis)构建变化向量;采用CCA为基础的多元变化检测MAD(multivariate alteration detection)最大限度的消除相关性影响,以方差最大的形式凸显耕地利用方式变化下耕地质量的变化;选择基于高斯混合模型(GMM)的期望最大化算法(EM)快速评价耕地利用方式变化下耕地质量的变化情况。重点分析基于快速评价的耕地利用方式变化下耕地质量变化区域的空间分布及其和传统因素法的对比分析。结果表明:基于变化向量法得到的耕地质量变化结果与传统的因素法在监测耕地质量水平不变的吻合度在80%~100%以上,耕地质量水平提高的吻合度在16.6%~50%,耕地质量水平下降的吻合度在66.7%~100%。提出的基于变化向量法的耕地质量变化评价模型能对耕地利用方式变化下耕地的质量变化做出正确的评价,评价方法科学、合理、易操作推广,为合理优化配置耕地利用方式提供定量数据支撑。

Evaluation of cultivated land quality under changed cultivated land use pattern based on change vector analysis

The quantity and quality of cultivated land plays an important role in our country's food security and the long-term stability of the community.It is important to assess the change of cultivated land quality under the changed cultivated land use pattern.This study aims to explore an effective method for detecting the changed information of cultivated land quality at county-level.Songyang County,Zhejiang province was selected as the study area to evaluate the cultivated land quality based on change vector analysis.The evaluation method was supported by Remote Sensing data and spatial analysis of GIS.There are obvious differentiation in space of cultivated land use patterns of Songyang County between 2006 and 2013.Among them,the Songgu Basin mainly planted economic crop tea,while the rice planting was scattered around the whole county.The areas where the cultivated land use pattern changed significantly were mainly concentrated in the Songgu Basin.18 pairs of“tea-to-rice”types and 33 pairs of“rice-to-tea”were selected to analyze the changes of cultivated land quality.Combined with the characteristics of Songyang county cultivated land,the object evaluation index system was constructed from the aspects of natural factors,human factors and location factors with the principles of productivity,feasibility,typicality and dominance.In this study,the change vector was used to highlight the change information of cultivated land quality.At the same time,the proposed canonical correlation analysis(CCA)based multivariate alteration detection(MAD)method was applied to minimize the correlation effect to the greatest extent,and highlight the changing of cultivated land quality under the changed cultivated land use pattern in the form of maximizing variance.It was a feasible way for monitoring the change of cultivated land quality.In addition,expectation maximization algorithm based on Gaussian mixture model(EM-GMM)was used to label the change of cultivated land quality.The results of MAD demonstrated the change information of cultivated land quality.The closer the value of the MAD result is to 0,the smaller the difference between the cultivated land quality between 2006 and 2013.The greater the difference of the index of cultivated land quality between different phases,the more likely the quality of cultivated land changes.The variables of MAD3 and MAD1 were used to analyze the effects of changed cultivated land use pattern on the quality of cultivated land,such as rice-to-tea and tea-to-rice.The quantitative proportion of the changed cultivated land quality level was compared by using two different methods,change vector analysis(CVA)and agricultural land classification method(national grade).The agreement between two methods in monitoring the quality of cultivated land was more than 80%-100%,the matching degree of increased cultivated land quality was 16.6%-50%,and the matching degree of declined cultivated land quality was66.7%-100%.Results demonstrated that the proposed change vector based change detection of cultivated land quality provide a quantitative data support for the rational optimization and allocation of cultivated land use pattern,which is significantly maintaining the stable quality and the sustainable development of cultivated land.Further research is needed on other scales,such as city,province.The proposed method should be further tested in other regions containing more complex conditions.