Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

Guangsheng Zhou,Zhao Jiang,Chufeng Wang,Jing Xie,Zhibang Luo,Chenghai Yang,Jian Zhang,Tianjin Xie,Tao Hu

doi:10.3390/rs12071207

Abstract

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

Highlights

Nowadays, the food security challenge has become a major concern for many countries and regions in the light of changing climatic conditions, political instabilities, and increasing consumption of resources [1]
Many studies have reported that GreenSeeker (GS), ASD handheld spectrometers, plant canopy analyzer and other field-based remote sensing sensors can accurately acquire the normalized difference vegetation index (NDVI), leaf area index (LAI) and other biophysical parameters related to crop growth [5,6,7,8]
For GS-NDVI and LAI estimation, the ASD-based near infrared (NIR)-vegetation indices (VIs) and red edge (RE)-VI were found to be better than the ASD-based RGB-VIs, which was consistent with the unmanned aerial vehicle (UAV)-based result

Summary

Introduction

The food security challenge has become a major concern for many countries and regions in the light of changing climatic conditions, political instabilities, and increasing consumption of resources [1]. Many studies have reported that GreenSeeker (GS), ASD handheld spectrometers, plant canopy analyzer and other field-based remote sensing sensors can accurately acquire the normalized difference vegetation index (NDVI), LAI and other biophysical parameters related to crop growth [5,6,7,8]. These methods still require manual operation and are labor-intensive for extensive sampling processes. Airborne and spaceborne remote-sensing technologies have been applied to a wide range of crop growth monitoring for decades [9], but the image resolution from this technology is too low to measure crop growth on fine scales [10]

Results

Discussion

Conclusion