农业工程学报2018，Vol.34Issue(8)：184-191,8.DOI:10.11975/j.issn.1002-6819.2018.08.024
基于GF-1影像NDVI年度间相关分析的冬小麦面积变化监测
Area change monitoring of winter wheat based on relationship analysis of GF-1 NDVI among different years
摘要
Abstract
In order to achieve fast monitoring of regional winter wheat area change, reduce monitoring difficulty, and improve monitoring efficiency and accuracy, the paper proposes a monitoring method based on the relationship analysis of normal difference vegetation index (rNDVI). By selecting 3 counties i.e. Huanghua, Mengcun, and Haixing County, Hebei Province as the study area, and by taking GF-1/WFV data of 2 dates i.e. April 14th, 2014 and April 26th, 2017, the paper conducted the monitoring in the study area by extracting the increased and decreased areas of winter wheat planting areas. Based on rNDVI, the paper built a two-dimensional space with two-year NDVI values of sample points, and thus obtained the monitoring threshold values of the changing areas of the winter wheat by employing the least squares fit method to obtain the upper and lower envelope equations of unchanged ground objects. The result shows that, the overall accuracy of rNDVI algorithm is 90.60%, with the Kappa coefficient of 0.84. Compared with the traditional method of making maximum likelihood classification and then extracting changed winter wheat planting areas, the overall accuracy of this method and its Kappa coefficient are improved by 6.6 percentages and 16.7% respectively. Analysis of the monitoring results on the change of the winter wheat increased area and decreased area shows that, the monitoring method based on rNDVI can effectively improve the identification ability on the change of the land areas such as bare land, linear roads, and fragmented winter wheat areas, and improve monitoring accuracy. The monitoring on the winter wheat changed area was conducted based on 2 pairs of GF-1/WFV data of March 1st, 2014 and March 12th, 2017, as well as May 17th, 2014 and May 20th, 2017. The result shows that the monitoring accuracy of March is relatively low, and the overall accuracies of May and April are close. The above study results show that, fast monitoring method of winter wheat change based on rNDVI can effectively monitor the change of the regional winter wheat planting area. The algorithm used in this method is simple and effective, and it can maintain relatively high accuracy for the sample planting structure of winter wheat planting area, and it can also meet the demand for fast acquisition of crop remote sensing monitoring information.关键词
农作物/遥感/识别/GF-1/NDVI/冬小麦/相关分析/变化检测Key words
crops/remote sensing/recognition/GF-1/NDVI/winter wheat/relationship analysis/change detection分类
农业科技引用本文复制引用
王利民,刘佳,姚保民,季富华,杨福刚..基于GF-1影像NDVI年度间相关分析的冬小麦面积变化监测[J].农业工程学报,2018,34(8):184-191,8.基金项目
国家重点研发计划"粮食作物生长监测诊断与精确栽培技术"课题"作物生长与生产力卫星遥感监测预测"(2016YFD0300603) （2016YFD0300603）
