Area change monitoring of winter wheat based on relationship analysis of GF-1 NDVI among different years

被引：0

作者：

Wang L. ^{[1
]}

Liu J. ^{[1
]}

Yao B. ^{[1
]}

Ji F. ^{[1
]}

Yang F. ^{[1
]}

机构：

[1] Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing

来源：

Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2018年 / 34卷 / 08期

关键词：

Change detection; Crops; GF-1; NDVI; Recognition; Relationship analysis; Remote sensing; Winter wheat;

D O I：

10.11975/j.issn.1002-6819.2018.08.024

中图分类号：

学科分类号：

摘要：

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. © 2018, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.

引用

页码：184 / 191

页数：7

共 35 条

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