Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method

Liu, Duanyang; Jia, Kun; Jiang, Haiying; Xia, Mu; Tao, Guofeng; Wang, Qingqing; Chen, Zhulin; Yuan, Bo; Li, Jie

doi:10.3390/rs13112165

Cited by 11 publications

(6 citation statements)

References 59 publications

(75 reference statements)

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“…Table 3 shows the random points assigned to each category of the classification results of the two periods. The total sample sizes for 2006 and 2019 were 794 and 400, respectively, calculated according to Equation (9). For the rarest classes (classes covering less than 5% of the map), a fixed number is assigned directly.…”

Section: Estimating Fvc and Accuracy Assessmentmentioning

confidence: 99%

“…Based on the radiative transfer model and machine learning algorithms, Landsat data can be used for quantitative mapping of vegetation coverage in space and time to estimate FVC with satisfactory accuracy [8]. The random forest regression method in combination with FengYun-3 reflectance data generated an FVC estimate with a reliable spatiotemporal continuity and high accuracy [9]. Although satellite data are suitable for estimating vegetation cover on large scales, it is generally difficult to generate coverage estimates for different species, especially in areas with sparse vegetation, e.g., deserts.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Fractional Vegetation Cover Changes in Desert Regions Using RGB Data

Xie

Zhao²,

Fu³

et al. 2022

Remote Sensing

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Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. Both satellite remote sensing and ground measurements are common methods for estimating FVC. However, desert vegetation grows sparsely and scantly and spreads widely in desert regions, making it challenging to accurately estimate its vegetation cover using satellite data. In this study, we used RGB images from two periods: images from 2006 captured with a small, light manned aircraft with a resolution of 0.1 m and images from 2019 captured with an unmanned aerial vehicle (UAV) with a resolution of 0.02 m. Three pixel-based machine learning algorithms, namely gradient enhancement decision tree (GBDT), k-nearest neighbor (KNN) and random forest (RF), were used to classify the main vegetation (woody and grass species) and calculate the coverage. An independent data set was used to evaluate the accuracy of the algorithms. Overall accuracies of GBDT, KNN and RF for 2006 image classification were 0.9140, 0.9190 and 0.9478, respectively, with RF achieving the best classification results. Overall accuracies of GBDT, KNN and RF for 2019 images were 0.8466, 0.8627 and 0.8569, respectively, with the KNN algorithm achieving the best results for vegetation cover classification. The vegetation coverage in the study area changed significantly from 2006 to 2019, with an increase in grass coverage from 15.47 ± 1.49% to 27.90 ± 2.79%. The results show that RGB images are suitable for mapping FVC. Determining the best spatial resolution for different vegetation features may make estimation of desert vegetation coverage more accurate. Vegetation cover changes are also important in terms of understanding the evolution of desert ecosystems.

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Section: Estimating Fvc and Accuracy Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Fractional Vegetation Cover Changes in Desert Regions Using RGB Data

Xie

Zhao²,

Fu³

et al. 2022

Remote Sensing

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show abstract

“…Second, Liu et al [34] developed a fractional vegetation cover estimation algorithm for Fengyun-3 (FY-3) reflectance data. For this algorithm, the PROSAIL model was selected to simulate high-quality training samples, including the simulated red and near-infrared (NIR) reflectance data and the corresponding fractional vegetation cover values.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Advances in Land–Ocean Heat Fluxes Using Remote Sensing

et al. 2022

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Advanced remote sensing technology has provided spatially distributed variables for estimating land–ocean heat fluxes, allowing for practical applications in drought monitoring, water resources management, and climate assessment. This Special Issue includes several research studies using state-of-the-art algorithms for estimating downward longwave radiation, surface net radiation, latent heat flux, columnar atmospheric water vapor, fractional vegetation cover, and grassland aboveground biomass. This Special Issue intends to help scientists involved in global change research and practices better comprehend the strengths and disadvantages of the application of remote sensing for monitoring surface energy, water, and carbon budgets. The studies published in this Special Issue can be applied by natural resource management communities to enhance the characterization and assessment of land–ocean biophysical variables, as well as for more accurately partitioning heat flux into soil and vegetation based on the existing and forthcoming remote sensing data.

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“…The initial data from 2014 to 2019 were the reflectance data of the PROBA-VEGRTATION sensor as model input data (since 2020, replaced with Sentinel-3 observations). CYCLOPE FVC products corrected by scaling coefficients were used as neural network model training samples [44]. A single GEOV3 image was obtained by the MVC method, and its spatial resolution was resampled to 500 m via the bilinear interpolation method.…”

Section: Geov3 Fvcmentioning

confidence: 99%

Multi-Scale Validation and Uncertainty Analysis of GEOV3 and MuSyQ FVC Products: A Case Study of an Alpine Grassland Ecosystem

et al. 2022

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Fractional vegetation cover (FVC) products provide essential data support for ecological environmental monitoring and simulation studies. However, the lack of validation efforts of FVC products limits their applications. Based on Sentinel-2 data and intensive multi-scale measured FVC data, the accuracies of two FVC products (GEOV3 and MuSyQ) in alpine grassland ecosystems were validated through direct validation and multi-scale validation. Based on the heterogeneity of the underlying surface (HUS) of the monitoring plots, the impact of the HUS of the monitoring plots on the product validation was analyzed. The results showed that: (1) the measured data directly validated that the GEOV3 FVC product performed better than the MuSyQ FVC product; (2) the multi-scale validation method based on high-resolution reference FVC map of Sentienl-2 satellite images validated the accuracy of these two FVC products, which was higher than the accuracy directly validated by FVC measured data, leading to overestimation of the validation results; and (3) the HUS of the monitored plots has a significant impact on the FVC product validation. By quantifying the HUS of the monitored plots and removing the heterogeneous monitoring plots, the uncertainty of the validation results can be reduced. It is necessary to consider the impact of validation methods and the HUS on the validation results in future product validation.

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Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method

Cited by 11 publications

References 59 publications

Estimating Fractional Vegetation Cover Changes in Desert Regions Using RGB Data

Estimating Fractional Vegetation Cover Changes in Desert Regions Using RGB Data

Advances in Land–Ocean Heat Fluxes Using Remote Sensing

Multi-Scale Validation and Uncertainty Analysis of GEOV3 and MuSyQ FVC Products: A Case Study of an Alpine Grassland Ecosystem

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