Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

Yaxiao Niu,Xingshuo Peng,Liyuan Zhang,Wenting Han,Huihui Zhang

doi:10.3390/rs11111261

Abstract

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

Highlights

Agriculture is facing a great challenge in how to meet the increasing demand for agricultural production with limited soil and water resources due to the increasing population and rapidly growing economy [1]
The results showed that plant height directly derived from unmanned aerial vehicle (UAV) RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an root mean squared error (RMSE) value of 0.12 m
When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82

Summary

Introduction

Agriculture is facing a great challenge in how to meet the increasing demand for agricultural production with limited soil and water resources due to the increasing population and rapidly growing economy [1]. Han et al [38] estimated AGB of maize in Changping District of Beijing City, China and Li et al [39] estimated AGB of sorghum in Central City, Nebraska, US by simultaneously using VIs derived from UAV multispectral imagery and PH derived from UAV RGB imagery Many of these studies utilized VIs and PH derived from different UAV platforms. Research simultaneously using VIs and PH both derived from consumer-grade UAV RGB remote-sensing system is still relatively less in biomass estimation. The main goal of this study was to estimate AGB of maize by using VIs and PH both derived from consumer-grade UAV RGB imagery. (1) directly derive maize PH from consumer-grade UAV RGB point clouds and comparatively analyze the estimation performance with ground-truth PH; Remote Sens. Remote Sens. 2019, 11, x FOR PEER REVIEW (2) establish maize AGB estimation models based on PH alone by using linear and exponential (2)regesrteasbsliioshn manaaizlyesAesG; BbaessetidmoantioVnIsmaoldoenlse bbayseudsionng PsHingalleonaendbymuusilntigvalirniaebarlealnindeeaxrproengernetsisailon anraelgyrseesss;ioandanbaalsyesdeso; nbabsoetdh VonIsVaInsdaPloHnebybyusuinsigngmsuinltgivlearaianbdlemliunlteiavrarieagbrleesslinoenaar nraelgyrseisss;ion (3) coamnpalayrsaetsi;vaenlyd baansaeldyozen bthoteh pVeIsrfaonrdmPaHncbeys uosfinmg amizueltiAvaGriBabelestliimneaatrinreggrmesosidoenlsanaanlydsism; ap the (3)dicsotrmibpuatriaotnivoeflymaanizaelyAzeGBthbey puesrifnogrmthaenocepstimofalmeasitzime aAtiGnBg mesotdimela.ting models and map the distribution of maize AGB by using the optimal estimating model

Research Field

Field Measurements

Findings

Estimation Models of Plant Height and Biomass