Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

Sibanda, Mbulisi; Mutanga, Onisimo; Rouget, Mathieu; Kumar, Lalit

doi:10.3390/rs9010055

Cited by 56 publications

(43 citation statements)

References 70 publications

(84 reference statements)

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“…Then they extracted 8 × 8 pixels from a worldview2 image (2 m) around the center coordinate of each plot, which covered a 16 × 16 m 2 area, and used the average values of the 64 pixels. Sibanda et al [74] set 54 plots measuring 13.7 × 18.3 m 2 . They randomly extracted 20 pixels from each plot to represent the spectral characteristics of the plot and got 1080 pixels by this way.…”

Section: Experimental Settingsmentioning

confidence: 99%

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Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2

Dong

Han

et al. 2020

Remote Sensing

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Above-ground biomass (AGB) directly relates to the productivity of forests. Precisely, AGB mapping for regional forests based on very high resolution (VHR) imagery is widely needed for evaluation of productivity. However, the diversity of variables and algorithms and the difficulties inherent in high resolution optical imagery make it complex. In this paper, we explored the potentials of the state-of-art algorithm convolutional neural networks (CNNs), which are widely used for its high-level representation, but rarely applied for AGB estimation. Four experiments were carried out to compare the performance of CNNs and other state-of-art Machine Learning (ML) algorithms: (1) performance of CNN using bands, (2) performance of Random Forest (RF), support vector regression (SVR), artificial neural network (ANN) on bands, and vegetation indices (VIs). (3) Performance of RF, SVR, and ANN on gray-level co-occurrence matrices (GLCM), and exploratory spatial data analysis (ESDA), and (4) performance of RF, SVR, and ANN based on all combined data and ESDA+VIs. CNNs reached satisfactory results (with R2 = 0.943) even with limited input variables (i.e., only bands). In comparison, RF and SVR with elaborately designed data obtained slightly better accuracy than CNN. For examples, RF based on GLCM textures reached an R2 of 0.979 and RF based on all combined data reached a close R2 of 0.974. However, the results of ANN were much worse (with the best R2 of 0.885).

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Section: Experimental Settingsmentioning

confidence: 99%

“…They randomly extracted 20 pixels from each plot to represent the spectral characteristics of the plot and got 1080 pixels by this way. The sampling method we used mostly based on Sibanda et al [74] and used the method of Adam et al [73] for validation.…”

Section: Experimental Settingsmentioning

confidence: 99%

Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2

Dong

Han

et al. 2020

Remote Sensing

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“…Remotely sensed estimates of C3 and C4 grasses AGB over time were derived using Sentinel 2 variables (presented in Table ) and the Sparse Partial Least Squares regression (SPLSR) model (Chun & Keles, ). The SPLSR is one of the robust and powerful models for estimating species AGB, due to its ability to overcome the challenges of multicollinearity and over‐fitting, by transforming the variables to new components (Abdel‐Rahman et al, ; Sibanda, Mutanga, Rouget, & Kumar, ). The detailed explanation on how the SPLSR works in relating species AGB and variables of interest can be found in for example, Sibanda, Mutanga, and Rouget () and Shoko, Mutanga, and Dube ().…”

Section: Methodsmentioning

confidence: 99%

Remotely sensed C3 and C4 grass species aboveground biomass variability in response to seasonal climate and topography

2019

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Seasonal climate and topography influence C3 and C4 grass species aboveground biomass (AGB). Climate change further threatens these grasses AGB, thereby compromising their ability to provide ecosystem goods and services. This emphasises the need to monitor their AGB for well‐informed management. New‐generation sensors, with improved resolution capabilities present an opportunity to explore C3 and C4 AGB. This study therefore investigated the response of remotely sensed C3 and C4 grasses AGB to seasonal climate and topography. Overall, the spatial and temporal responses of AGB due to seasonal climate and topography were observed across the study area. For example, in March, a marked increase in C4 AGB was associated with an increase in rainfall, with the highest significant positive relationship (R2 = 0.82, p < 0.005). Elevation had very significant positive relationship (R2 = 0.84; p < 0.005) with C3 and highest negative (R2 = −0.77; p < 0.005) with C4 AGB. During the winter fall, AGB significantly decreased from averages of 2.592 and 1.101 kg/m2 in winter (May), to 0.718 and 0.469 kg/m2 in August, for C3 and C4 grasses, respectively. These findings provide a key step in monitoring rangelands and assessing management practices to boost productivity.

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“…SPLSR is a robust and powerful algorithm for estimating vegetation biophysical properties using remote sensing data. So far, its high performance in predicting grass AGB across different environments has been reported [32][33][34]. The model builds estimation functions and associated variables using remote sensing datasets.…”

Section: Regression Algorithm For Predicting F Costata and T Triandmentioning

confidence: 99%

Determining Optimal New Generation Satellite Derived Metrics for Accurate C3 and C4 Grass Species Aboveground Biomass Estimation in South Africa

2018

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While satellite data has proved to be a powerful tool in estimating C3 and C4 grass species Aboveground Biomass (AGB), finding an appropriate sensor that can accurately characterize the inherent variations remains a challenge. This limitation has hampered the remote sensing community from continuously and precisely monitoring their productivity. This study assessed the potential of a Sentinel 2 MultiSpectral Instrument, Landsat 8 Operational Land Imager, and WorldView-2 sensors, with improved earth imaging characteristics, in estimating C3 and C4 grasses AGB in the Cathedral Peak, South Africa. Overall, all sensors have shown considerable potential in estimating species AGB; with the use of different combinations of the derived spectral bands and vegetation indices producing better accuracies. However, WorldView-2 derived variables yielded better predictive accuracies (R 2 ranging between 0.71 and 0.83; RMSEs between 6.92% and 9.84%), followed by Sentinel 2, with R 2 between 0.60 and 0.79; and an RMSE 7.66% and 14.66%. Comparatively, Landsat 8 yielded weaker estimates, with R 2 ranging between 0.52 and 0.71 and high RMSEs ranging between 9.07% and 19.88%. In addition, spectral bands located within the red edge (e.g., centered at 0.705 and 0.745 µm for Sentinel 2), SWIR, and NIR, as well as the derived indices, were found to be very important in predicting C3 and C4 AGB from the three sensors. The competence of these bands, especially of the free-available Landsat 8 and Sentinel 2 dataset, was also confirmed from the fusion of the datasets. Most importantly, the three sensors managed to capture and show the spatial variations in AGB for the target C3 and C4 grassland area. This work therefore provides a new horizon and a fundamental step towards C3 and C4 grass productivity monitoring for carbon accounting, forage mapping, and modelling the influence of environmental changes on their productivity.

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Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

Cited by 56 publications

References 70 publications

Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2

Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2

Remotely sensed C3 and C4 grass species aboveground biomass variability in response to seasonal climate and topography

Determining Optimal New Generation Satellite Derived Metrics for Accurate C3 and C4 Grass Species Aboveground Biomass Estimation in South Africa

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