基于TOPSIS和BP神经网络的高标准农田综合识别

Multi-characteristic Comprehensive Recognition of Well-facilitied Farmland Based on TOPSIS and BP Neural Network

为提高耕地综合生产能力,适应农业现代化发展需求,我国提出了高标准农田建设的重大战略部署。高标准农田的识别是建设前选址和建设后评价的基础。本文以耕地图斑为基本单元,融合遥感影像等多源数据,从本底条件、空间形态、建设水平、生态防护等方面,构建农田综合质量多特性表征体系,采用逼近理想点排序法(TOPSIS)进行初步评价,再以人机交互的方式选取各质量等级农田的真值样本,进一步采用BP神经网络算法修正各特性权值,得到农田综合质量的精确评价结果,实现高标准农田识别。以吉林省大安市为研究区,研究结果表明:基于多特性表征体系的农田综合质量评价方法精度达到96%以上;研究区高标准农田面积广大,主要分布在耕地集中连片、道路通达、生态防护良好、具有农业现代化生产优势的东北部、中北部、西北部边缘和部分南部区域;当地已备案的高标准农田和未备案、有潜力的高质量农田区域均得到有效识别。

China puts forward the major strategic deployment of constructing well-facilitied farmland vigorously to improve the overall production capacity of farmland and adapt to the development of agricultural modernization. The recognition of well-facilitied farmland is foundation of site selection before constructing and evaluation after constructing. The well-facilitied farmland was understood from the point of view of production demand and recognized based on the evaluation of farmland comprehensive quality.Firstly,the characteristics of farmland comprehensive quality was analyzed from a lot of angles,such as background condition,spatial shape,construction level,ecological protection and so on,by fusing the multi-source data and taking the farmland patches as the basic units. The description system of farmland comprehensive quality was built by using five characteristics, including soil productivity, land contiguous,field shape,road accessibility and ecological protection. Secondly,it assumed that these five characteristics were the same important for farmland comprehensive quality,so the weights were all made as 0. 20 and the preliminary evaluation results were got by TOPSIS method. Thirdly,the true-value samples were acquired by using the combined method of preliminary evaluation results and man-machine interactive optimization. The man-machine interactive optimization was achieved by spatial overlay between the preliminary evaluation results and the farmland utilization grade from the farmland-grading work in China. And then BP neural network was used to fix the feature weights. Fourthly,the final accurate comprehensive quality evaluation results were got and the recognition of the well-facilitied farmland was achieved. Finally,Daan City in Jilin Province was taken as the study area. The research results showed that the accuracy of the method to evaluate farmland comprehensive quality was above 96%,basing on the multi-characteristic description system. The well-facilitied farmland was widelydistributed in the study area. The well-facilitied farmland mainly concentrated in northeast,north,edge of northwest and part of the southern region. These regions had the advantage of agricultural modernization,such as concentrated farmland,villages,roads and forest. The well-facilitied farmland which was registered with the law and the prospective high-quality farmland which was not registered with the law were both recognized effectively. The above result had strong consistency on the spatial distribution with the preliminary evaluation results,but the former refined the comprehensive quality results of partial farmland based on the relative importance of each characteristic. The research result can provide scientific reference and technical support for regulation,protection and construction of wellfacilitied farmland.