Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season
Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most...
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MDPI AG
2021-07-01
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author | Kaili Yang Yan Gong Shenghui Fang Bo Duan Ningge Yuan Yi Peng Xianting Wu Renshan Zhu |
author_facet | Kaili Yang Yan Gong Shenghui Fang Bo Duan Ningge Yuan Yi Peng Xianting Wu Renshan Zhu |
author_sort | Kaili Yang |
collection | DOAJ |
description | Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R<sup>2</sup>) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth. |
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issn | 2072-4292 |
language | English |
last_indexed | 2024-03-10T09:09:18Z |
publishDate | 2021-07-01 |
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series | Remote Sensing |
spelling | doaj.art-8db133cbb9434caaa2954ad8b3f6c2ac2023-11-22T06:07:22ZengMDPI AGRemote Sensing2072-42922021-07-011315300110.3390/rs13153001Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing SeasonKaili Yang0Yan Gong1Shenghui Fang2Bo Duan3Ningge Yuan4Yi Peng5Xianting Wu6Renshan Zhu7School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaLab of Remote Sensing for Precision Phenomics of Hybrid Rice, Wuhan University, Wuhan 430079, ChinaLab of Remote Sensing for Precision Phenomics of Hybrid Rice, Wuhan University, Wuhan 430079, ChinaLeaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R<sup>2</sup>) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth.https://www.mdpi.com/2072-4292/13/15/3001leaf area index (LAI)unmanned aerial vehicle (UAV)multispectral imagevegetation index (VI)texturelocal binary pattern (LBP) |
spellingShingle | Kaili Yang Yan Gong Shenghui Fang Bo Duan Ningge Yuan Yi Peng Xianting Wu Renshan Zhu Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season Remote Sensing leaf area index (LAI) unmanned aerial vehicle (UAV) multispectral image vegetation index (VI) texture local binary pattern (LBP) |
title | Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season |
title_full | Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season |
title_fullStr | Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season |
title_full_unstemmed | Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season |
title_short | Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season |
title_sort | combining spectral and texture features of uav images for the remote estimation of rice lai throughout the entire growing season |
topic | leaf area index (LAI) unmanned aerial vehicle (UAV) multispectral image vegetation index (VI) texture local binary pattern (LBP) |
url | https://www.mdpi.com/2072-4292/13/15/3001 |
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