Assessing Ndvi Spatial Pattern Related to Management Zones

Abstract

The NDVI of GreenSeeker has proven to be an effective tool for Management Zoning. An optimal method on the basis of NDVI was proposed to establish Management Zoning accurately in this study. First, the coefficient of variation of time series distance difference of Greenseeker and UAV NDVI was tested, it was significant to evaluate the accuracy of Greenseeker NDVI for large-areas. On this basis, optimal partition numbers and partitioning algorithm were identified according to the Silhouette Coefficient of data structure, the accuracy of estimation of agronomic parameters and the difference of agronomic parameters in each plot under different partition numbers. Results demonstrate that the difference among the points of the distance sequence is not significant (F = 0.641 > 0.05). The NDVI of Greenseeker has high reliability. When the partitioning algorithm and the number of partitions are Kmeans and four, respectively, the Silhouette Coefficient and the accuracy of estimation of agronomic parameters (plant height, nitrogen content) are higher than in other treatments. At the same time, the agronomic parameters in each plot showed the significant differences. The proposed method provides a good prospect to optimize the establishment of management zones.

Method for Zoning Corn Based on the NDVI and the Improved SOM-K-Means Algorithm

“…Santos et al (2019) proposed a system that used the K-means clustering algorithm to delimit management areas based on the crop NDVI captured by unmanned aerial vehicles to achieve the real-time, rapid, and accurate division of farmland management areas. Liu and Wang (2019) utilized the NDVI of corn determined by drone remote sensing and ground remote sensing to establish an optimized zoning method for management areas based on the NDVI, plant height, and biomass of corn. Then, reasonable management zones were established using the NDVI based on a clustering algorithm.…”