Remote Estimation of Vegetation Fraction and Flower Fraction in Oilseed Rape with Unmanned Aerial Vehicle Data

Abstract

This study developed an approach for remote estimation of Vegetation Fraction (VF) and Flower Fraction (FF) in oilseed rape, which is a crop species with conspicuous flowers during reproduction. Canopy reflectance in green, red, red edge and NIR bands was obtained by a camera system mounted on an unmanned aerial vehicle (UAV) when oilseed rape was in the vegetative growth and flowering stage. The relationship of several widely-used Vegetation Indices (VI) vs. VF was tested and found to be different in different phenology stages. At the same VF when oilseed rape was flowering, canopy reflectance increased in all bands, and the tested VI decreased. Therefore, two algorithms to estimate VF were calibrated respectively, one for samples during vegetative growth and the other for samples during flowering stage. The results showed that the Visible Atmospherically Resistant Index (VARI green ) worked most accurately for estimating VF in flower-free samples with an Root Mean Square Error (RMSE) of 3.56%, while the Enhanced Vegetation Index (EVI2) was the best in flower-containing samples with an RMSE of 5.65%. Based on reflectance in green and NIR bands, a technique was developed to identify whether a sample contained flowers and then to choose automatically the appropriate algorithm for its VF estimation. During the flowering season, we also explored the potential of using canopy reflectance or VIs to estimate FF in oilseed rape. No significant correlation was observed between VI and FF when soil was visible in the sensor's field of view. Reflectance at 550 nm worked well for FF estimation with coefficient of determination (R 2 ) above 0.6. Our model was validated in oilseed rape planted under different nitrogen fertilization applications and in different phenology stages. The results showed that it was able to predict VF and FF accurately in oilseed rape with RMSE below 6%.

A Regional Mapping Method for Oilseed Rape Based on HSV Transformation and Spectral Features

“…Oilseed rape (Brassica napus L.) is a major cash crop, as well as one of the principal sources of edible oil, high energy, and protein meal plant species in countries with moderate climate (e.g., China, Europe, Canada, India, Australia, etc.) [1][2][3]. Accurate maps for oilseed rape (OR) distribution are essential to guide macro decision-making which maintains balance in agricultural supply and demand.…”

“…However, the shortages of small spatial coverage, as well as expensive cost of hyperspectral images, limit its application on regional OR mapping.The second category mainly applies supervised classification methods on multispectral images during the flowering period to identify and extract OR at the local scale, since the flowering period is the best phenology stage of identifying OR from other crops [11]. As a member of the Brassicaceae family, OR appears as bright-yellow flowers lasting 30 days (approximately a quarter of its entire growing season) [3,12], which leads to a large difference on the reflectance at green, red, and near-infrared bands when compared with other crop species during the same period because of the radiation reflected by the flower petals [13][14][15]. She et al [11] introduced the effectiveness of identifying OR from other crops during its flowering phase and the difficulty in other growing stages.…”

Robinia pseudoacacia L. Flower Analyzed by Using An Unmanned Aerial Vehicle (UAV)

“…The use of UAVs in agriculture [36][37][38]56,57,62], horticulture [51], plant ecology [66,68,89], and forestry [59,61,62,65] is increasing worldwide. However, UAVs were not heretofore used to quantify the flower production of black locust.…”

Estimating and Examining the Sensitivity of Different Vegetation Indices to Fractions of Vegetation Cover at Different Scaling Grids for Early Stage Acacia Plantation Forests Using a Fixed-Wing UAS

“…The UAS platform provides alternatives to space-borne platforms since optical data can be observed in a clear-high spatial/temporal resolution for the region of interest [23]. This technique has been used in research on ecology [24], precision agriculture/forestry [25,26] and even analyses for estimating vegetation cover [27][28][29]. UAS was successful [27] in clearly estimating vegetation fractions and flower fractions in crop fields with the changing VIs, and work by Chen et al [28] showed that utilizing UAS-captured imagery may clearly detect grassy vegetation covers due to its high-resolution data.…”

“…This technique has been used in research on ecology [24], precision agriculture/forestry [25,26] and even analyses for estimating vegetation cover [27][28][29]. UAS was successful [27] in clearly estimating vegetation fractions and flower fractions in crop fields with the changing VIs, and work by Chen et al [28] showed that utilizing UAS-captured imagery may clearly detect grassy vegetation covers due to its high-resolution data. Riihimäkia et al [29] showed that the UAV-derived information can be aided by satellite-observed information in FVC estimations.…”