Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass

Wu, Chen; Tao, Hongxiang; Zhai, Manyu; Lin, Yi; Wang, Ke; Deng, Jinsong; Shen, Aihua; Gan, Muye; Li, Jun; Yang, Hong

doi:10.1007/s11676-017-0404-9

Cited by 18 publications

(15 citation statements)

References 54 publications

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“…Based on the predictor selection in Section 3.2.1, the best predictors for forest AGB mapping were determined to be those listed in Table 3, which belong to the four variable groups, S1, S2, S and S + S. In contrast, the optimal modeling algorithm for predictors from the four variable groups were all the RF algorithm. This study revealed the powerful capacity of the RF algorithm to predict forest AGB, as in other reported studies [88,[91][92][93]. The SVR algorithm, however, performed the worst for S1 variables, while the SWR algorithm performed the worst for S2 and S, as well as S+S modeling.…”

Section: Optimal Combination Of Predictors and Modeling Algorithmssupporting

confidence: 83%

Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

et al. 2019

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Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR); machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mg·ha−1 and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms.

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Section: Optimal Combination Of Predictors and Modeling Algorithmssupporting

confidence: 83%

Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

et al. 2019

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“…SVR has proven to outperform conventional methods in environmental modeling [57][58][59][60][61], land-use and land-cover classification [62], and estimating forest biomass [32,63]. The main advantage of SVR is that it is highly accurate at predicting even with small numbers of training samples [64].…”

Section: Support Vector Regressionmentioning

confidence: 99%

“…In addition, the performance of the SVR model is significantly influenced by the selection of the kernel functions. Therefore, in this research, we selected the Radial Basis Function (RBF) kernel because it is the most widely used for determining forest biomass in previous studies [31,32,63]. Consequently, the training of the SVR model required finding the best values for the two meta-parameters, the regularization parameter (C), and the kernel width (γ).…”

Section: Support Vector Regressionmentioning

confidence: 99%

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

et al. 2018

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Abstract:The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R 2 ), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R 2 = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R 2 = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R 2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R 2 = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.

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“…Comparatively speaking, statistical methods realize this prediction processes by establishing relationships more directly. Among the most advanced approaches, machine learning methods have received considerable attention in recent years [14,15]. Compared to traditional regression algorithms such as multiple linear regression, machine learning algorithms have no strict assumptions on input variables or relationships between response variables and explanatory variables [16].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions

Shen

Jiang

et al. 2018

Forests

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Biomass is a key biophysical parameter used to estimate carbon storage and forest productivity. Spatially-explicit estimation of biomass provides invaluable information for carbon stock calculation and scientific forest management. Nevertheless, there still exists large uncertainty concerning the relationship between biomass and influential factors. In this study, aboveground biomass (AGB) was estimated using the random forest algorithm based on remote sensing imagery (Landsat) and field data for three regions with different topographic conditions in Zhejiang Province, China. AGB distribution and change combined with stratified terrain classifications were analyzed to investigate the relations between AGB and topography conditions. The results indicated that AGB in three regions increased from 2010 to 2015 and the magnitude of growth varied with elevation, slope, and aspect. In the basin region, slope had a greater influence on AGB, and we attributed this negative AGB-elevation relationship to ecological forest construction. In the mountain area, terrain features, especially elevation, showed significant relations with AGB. Moreover, AGB and its growth showed positive relations with elevation and slope. In the island region, slope also played a relatively more important role in explaining the relationship. These results demonstrate that AGB varies with terrain conditions and its change is a consequence of interactions between the natural environment and anthropogenic behavior, implying that biomass retrieval based on Landsat imagery could provide considerable important information related to regional heterogeneity investigations.

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Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass

Cited by 18 publications

References 54 publications

Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions

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