Improved estimation of aboveground biomass in rubber plantations by fusing spectral and textural information from UAV-based RGB imagery

Liang, Yuying; Kou, Weili; Lai, Hongyan; Wang, Juan; Wang, Qiuhua; Xu, Weiheng; Wang, Huan; Lu, Nanshu

doi:10.1016/j.ecolind.2022.109286

Cited by 39 publications

(43 citation statements)

References 57 publications

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“…Orientation was less influential on the correlation between texture features and LAI and LCC in this study, with 45° outperforming the other three angles in most cases. This is consistent with most previous studies (Liang et al, 2022;Sibiya et al, 2021). However, our results differ from those of Pu and Chen et al (2015), who showed that the 90° orientation was used to compute the eight second-order texture metrics for all WorldView-2 images, which resulted in the highest accuracy of LAI estimation, R 2 = 0.82.…”

Section: Discussionsupporting

confidence: 87%

“…Zhang et al (2021) also found that the machine learning RF method has a more robust capability than MLR in terms of handling different remote sensing indices when estimating crop growth parameters using UAV, which further reduced the RRMSE by 2.74 -5.11% in the estimation of LAI and LDW. However, Estimating LAI, LCC at multiple growth stages using RGB optical vegetation indices has drawbacks and limitations (Lu, 2006;Liang et al, 2022): (i) vegetation indices features of crops are easily obscured or saturated under high canopy cover, e.g. correlation of spectral indices with LAI, LCC was significantly lower at multiple growth reproductive stages (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Image texture is expressed by the greyscale distribution of pixels and their surrounding spatial neighborhood. Texture features extracted based on gray-level co-occurrence matrices have been widely used in estimating leaf area index, yield and biomass (Hall-Beyer, 2017;Li et al, 2020;Liang et al, 2022). GLCM extracts the relevant texture information of an image by considering the relative positions of pixels in the image and generating corresponding matrices to describe the spatial gray-level dependencies between image pixels (Luo et al, 2020;Zhang et al, 2021).…”

Section: Table 1 Selection and Extraction Of Vegetation Indicesmentioning

confidence: 99%

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Estimate leaf area index and leaf chlorophyll content in winter-wheat using image texture and vegetation indices derived from multi-temporal RGB images

Yu,

Huo,

et al. 2023

Preprint

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Aims Investigating the potential of combining data dimensionality reduction methods with various linear regression models and machine learning algorithms to improve the accuracy of leaf area index (LAI) and chlorophyll content (LCC) estimation in winter wheat based on UAV RGB imagery. Methods Constructed and compared the performance of three linear regression techniques: multiple linear regression (MLR), ridge regression (RR), and partial least squares regression (PLSR) and three machine learning algorithms: back-propagation neural networks(BP), random forests (RF) and support vector regression (SVR) with spectral vegetation indices (VIs), texture features (TEs) and their combinations extracted from UAV RGB images. Moreover, different data dimensionality reduction methods include principal component analysis (PCA), and stepwise selection (ST) were used to improve the accuracy of LAI and LCC estimation. Results The highest correlation between texture features and LAI, LCC was obtained with window size 5 × 5, orientation 45° and displacement 2 pixels. Combining VIs and TEs improved the accuracy of LAI and LCC estimation for wheat compared to using VIs or TEs alone. The RF model combined with ST_PCA for fusing VIs and TEs achieved the best estimations, with R2 of 0.86 and 0.91, RMSE of 0.26 and 2.01, and MAE of 0.22 and 1.66 for LAI and LCC, respectively. Conclusions The fusing of multiple features improved the accuracy of LAI and LCC estimation. ST_PCA, combined with machine learning algorithms, holds promising potential for monitoring crop physiological and biochemical parameters.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Table 1 Selection and Extraction Of Vegetation Indicesmentioning

confidence: 99%

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Estimate leaf area index and leaf chlorophyll content in winter-wheat using image texture and vegetation indices derived from multi-temporal RGB images

Yu,

Huo,

et al. 2023

Preprint

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“…Compared with the LIDAR sensor, RGB sensors mounted on a UAV are more suitable for large scale vegetation parameter measurements. Therefore, they have been widely used in previous studies [34][35][36][37][38]. In a past study [35], scholars extracted the crown pixels of UAV RGB images, and used the pixel-segmented algorithm and the half-Gaussian fitting algorithm to calculate the crown coverage, which demonstrated the capability of this method (R 2 = 0.72).…”

Section: Introductionmentioning

confidence: 99%

“…In a past study [35], scholars extracted the crown pixels of UAV RGB images, and used the pixel-segmented algorithm and the half-Gaussian fitting algorithm to calculate the crown coverage, which demonstrated the capability of this method (R 2 = 0.72). Another study by Liang et al [36] integrated texture features with textural parameters and spectral information of RGB images from a UAV using the support vector machine (SVM) algorithm to estimate the above-ground biomass (AGB) of rubber plantations, and achieved an excellent estimation accuracy (R 2 = 0.75). In addition, Mariana de Jesús Marcial-Pablo et al [37] constructed the visible vegetation indices, including the Excess Green Index (EXG), Color Index of Vegetation (CIVE), and Vegetation Index Green (VIG), to estimate the vegetation fraction at the National Institute of Forestry, based on UAV RGB images, which resulted in a high average user accuracy (85.66%).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Three-Dimension Green Volume Based on UAV RGB Images: A Case Study in YueYaTan Park in Kunming, China

Hong

Liu

et al. 2023

Forests

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Three-dimension green volume (3DGV) is a quantitative index that measures the crown space occupied by growing plants. It is often used to evaluate the environmental and climatic benefits of urban green space (UGS). We proposed the Mean of neighboring pixels (MNP) algorithm based on unmanned aerial vehicle (UAV) RGB images to estimate the 3DGV in YueYaTan Park in Kunming, China. First, we mapped the vegetated area by the RF algorithm based on visible vegetation indices and texture features, which obtained a producer accuracy (PA) of 98.24% and a user accuracy (UA) of 97.68%. Second, the Canopy Height Mode (CHM) of the vegetated area was built by using the Digital Surface Model (DSM) and Digital Terrain Model (DTM), and the vegetation coverage in specific cells (1.6 m × 1.6 m) was calculated based on the vegetation map. Then, we used the Mean of neighboring pixels (MNP) algorithm to estimate 3DGV based on the cell area, canopy height, and vegetation coverage. Third, the 3DGV based on the MNP algorithm (3DGV_MNP), the Convex hull algorithm (3DGV_Con), and the Voxel algorithm (3DGV_Voxel) were compared with the 3DGV based on the field data (3DGV_FD). Our results indicate that the deviation of 3DGV_MNP for plots (Relative Bias = 15.18%, Relative RMSE = 19.63%) is less than 3DGV_Con (Relative Bias = 24.12%, Relative RMSE = 29.56%) and 3DGV_Voxel (Relative Bias = 30.77%, Relative RMSE = 37.49%). In addition, the deviation of 3DGV_MNP (Relative Bias = 17.31%, Relative RMSE = 19.94%) is also less than 3DGV_Con (Relative Bias = 24.19%, Relative RMSE = 25.77%), and 3DGV_Voxel (Relative Bias = 27.81%, Relative RMSE = 29.57%) for individual trees. Therefore, it is concluded that the 3DGV estimation can be realized by using the Neighboring pixels algorithm. Further, this method performed better than estimation based on tree detection in UGS. There was 377,223.21 m3 of 3DGV in YueYaTan Park. This study provides a rapid and effective method for 3DGV estimation based on UAV RGB images.

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Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review

2023

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Improved estimation of aboveground biomass in rubber plantations by fusing spectral and textural information from UAV-based RGB imagery

Cited by 39 publications

References 57 publications

Estimate leaf area index and leaf chlorophyll content in winter-wheat using image texture and vegetation indices derived from multi-temporal RGB images

Estimate leaf area index and leaf chlorophyll content in winter-wheat using image texture and vegetation indices derived from multi-temporal RGB images

Estimation of the Three-Dimension Green Volume Based on UAV RGB Images: A Case Study in YueYaTan Park in Kunming, China

Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review

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