Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier

Lin Zhang,Shaoming Li,Tianying Zhou,Diyou Liu,Tianwei Ren,Chao Zhang,Liang Tong,Zhe Liu,Xiaodong Zhang,Zhe Ma

doi:10.3390/rs12030362

Abstract

Seed maize and common maize plots have different planting patterns and variety types. Identification of seed maize is the basis for seed maize growth monitoring, seed quality and common maize seed supply. In this paper, a random forest (RF) classifier is used to develop an approach for seed maize fields’ identification, using the time series vegetation indexes (VIs) calculated from multispectral data acquired from Landsat 8 and Gaofen 1 satellite (GF-1), field sample data, and texture features of Gaofen 2 satellite (GF-2) panchromatic data. Huocheng and Hutubi County in the Xinjiang Uygur Autonomous Region of China were chosen as study area. The results show that RF performs well with the combination of six VIs (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), triangle vegetation index (TVI), ratio vegetation index (RVI), normalized difference water index (NDWI) and difference vegetation index (DVI)) and texture features based on a grey-level co-occurrence matrix. The classification based on “spectrum + texture” information has higher overall, user and producer accuracies than that of spectral information alone. Using the “spectrum + texture” method, the overall accuracy of classification in Huocheng County is 95.90%, the Kappa coefficient is 0.92, and the producer accuracy for seed maize fields is 93.91%. The overall accuracy of the classification in Hutubi County is 97.79%, the Kappa coefficient is 0.95, and the producer accuracy for seed maize fields is 97.65%. Therefore, RF classifier inputted with high-resolution remote-sensing image features can distinguish two kinds of planting patterns (seed and common) and varieties types (inbred and hybrid) of maize and can be used to identify and map a wide range of seed maize fields. However, this method requires a large amount of sample data, so how to effectively use and improve it in areas lacking samples needs further research.

Highlights

Accurate crop-type distribution data is a prerequisite for monitoring crop growth and yield forecasting [1,2,3,4,5,6]
We discarded soil regulation vegetation index (SAVI) and green normalized difference vegetation index (GNDVI), the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), triangle vegetation index (TVI), ratio vegetation index (RVI), normalized difference water index (NDWI), difference vegetation index (DVI) were selected as the input data in the final classification
The aim of this paper is to explore the feasibility of high-resolution remote-sensing satellite images in identifying different planting patterns and varieties of the same crop, and expand the connotation of crop precise classification by remote sensing

Summary

Introduction

Accurate crop-type distribution data is a prerequisite for monitoring crop growth and yield forecasting [1,2,3,4,5,6]. Satellite-based remote sensing has been widely used in the identification and mapping of different land-use types and crop types, by selecting the appropriate classification features and methods [7,8]. For multi-temporal features using, Jakubauskas [12] and Geerken [15] identify crop types and calculate coverage based on the NDVI time series. Ma [19] compared the classification results of single-phase and multi-phase and found that the information obtained from multi-phase remote sensing data can greatly improve the classification accuracy. Liu [20] and Hao [23] studied the methods of crop classification based on monthly and 14 time-phased time series data of NDVI in Hengshui City, Hebei Province, and the three northeast provinces, respectively. Selecting appropriate classification characteristics can improve the calculation efficiency, and obtain higher classification accuracy

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Results

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Conclusion