Monitoring of maize chlorophyll content based on multispectral vegetation indices

Hong Sun; Minzan Li; Lihua Zheng; Yane Zhang; Yajing Zhang

doi:10.1117/12.977414

9 November 2012 Monitoring of maize chlorophyll content based on multispectral vegetation indices

Hong Sun, Minzan Li, Lihua Zheng, Yane Zhang, Yajing Zhang

Proceedings Volume 8527, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV; 852711 (2012) https://doi.org/10.1117/12.977414
Event: SPIE Asia-Pacific Remote Sensing, 2012, Kyoto, Japan

Abstract

In order to estimate the nutrient status of maize, the multi-spectral image was used to monitor the chlorophyll content in the field. The experiments were conducted under three different fertilizer treatments (High, Normal and Low). A multispectral CCD camera was used to collect ground-based images of maize canopy in green (G, 520~600nm), red (R, 630~690nm) and near-infrared (NIR, 760~900nm) band. Leaves of maize were randomly sampled to detect the chlorophyll content by UV-Vis spectrophotometer. The images were processed following image preprocessing, canopy segmentation and parameter calculation: Firstly, the median filtering was used to improve the visual contrast of image. Secondly, the leaves of maize canopy were segmented in NIR image. Thirdly, the average gray value (G_IA, R_IA and NIR_IA) and the vegetation indices (DVI, RVI, NDVI, et al.) widely used in remote sensing were calculated. A new vegetation index, combination of normalized difference vegetation index (CNDVI), was developed. After the correlation analysis between image parameter and chlorophyll content, six parameters (G_IA, R_IA, NIR_IA, GRVI, GNDVI and CNDVI) were selected to estimate chlorophyll content at shooting and trumpet stages respectively. The results of MLR predicting models showed that the R² was 0.88 and the adjust R² was 0.64 at shooting stage; the R² was 0.77 and the adjust R² was 0.31 at trumpet stage. It was indicated that vegetation indices derived from multispectral image could be used to monitor the chlorophyll content. It provided a feasible method for the chlorophyll content detection.

Citation Download Citation

Hong Sun, Minzan Li, Lihua Zheng, Yane Zhang, and Yajing Zhang "Monitoring of maize chlorophyll content based on multispectral vegetation indices", Proc. SPIE 8527, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV, 852711 (9 November 2012); https://doi.org/10.1117/12.977414

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KEYWORDS

Vegetation

Image segmentation

Near infrared

Image filtering

Image processing

Nitrogen

Digital filtering

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