Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery
| Autor: | Haitao Xiang, Changwen Du, Fei Ma, Zhengchao Qiu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Index (economics)
Coefficient of determination 010504 meteorology & atmospheric sciences 0211 other engineering and technologies Rice growth 02 engineering and technology 01 natural sciences estimation accuracy growth indicators Statistics unmanned aerial vehicle Leaf area index lcsh:Science 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics Biomass (ecology) object-oriented segmentation method optimal index method rice multi-stage vegetation index food and beverages Vegetation General Earth and Planetary Sciences RGB color model lcsh:Q Stage (hydrology) |
| Zdroj: | Remote Sensing, Vol 12, Iss 3228, p 3228 (2020) Remote Sensing; Volume 12; Issue 19; Pages: 3228 |
| ISSN: | 2072-4292 |
| Popis: | The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production. |
| Databáze: | OpenAIRE |
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