Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

Laurin, Gaia Vaglio; Pirotti, Francesco; Callegari, Mattia; Chen, Qi; Cuozzo, Giovanni; Lingua, Emanuele; Notarnicola, Claudia; Papale, Dario

doi:10.3390/rs9010018

Cited by 58 publications

(30 citation statements)

References 57 publications

(71 reference statements)

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“…Among available variables, perplexity, Shannon index, and entropy were found most correlated with the referenced aboveground biomass with R 2 0.67, 0.67, and 0.66, respectively; these results are in line with the results obtained in published literature [31][32][33][34][35][36]. Detailed regression results are shown in Table 3.…”

Section: Resultssupporting

confidence: 87%

Retrieval of Tropical Peatland Forest Biomass From Polarimetric Features in Central Kalimantan, Indonesia

Waqar¹,

Sukmawati²,

Ji³

et al. 2020

PIER C

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In this research, the potential of L-band SAR data is evaluated for tropical peatland forest biomass estimation using polarimetric features and field data. For this, ALOS-2 full polarimetric data are acquired over central Kalimantan, Indonesia. Total 54 sampled plots (20 m × 20 m) were established in the study site; diameter at breast height (DBH) and tree species of every tree were collected in each plot. Locally developed allometric equations were used to convert field data to biomass and plot level biomass, and the upscaling factor was applied to upscale plot level biomass to standard tones per hectare scale. Backscattering coefficient (σ o) was computed for HH, HV, V H and V V polarization. Similarly, eigen decomposition was performed to extract: entropy (E), alpha (α), and anisotropy (A); also diversity indices were computed. Yamaguchi decomposition was performed to extract scattering behavior of forest in central Kalimantan. All polarimetric parameters were upscaled to one-hectare scale. Field data were divided into training plots (70 percent → 42 plots) and validation plots (30 percent → 12 plots). Nonlinear regression analysis was performed between polarimetric parameters and training plots. Perplexity, Shannon index, entropy, Gini Simpson index, index of qualitative inversion, Reyni entropy (order 2), σ HV , alpha, σ V V , and volumetric scattering component were found significantly correlated (ranging R 2 from 0.67 to 0.49) with the field data. The corresponding nonlinear model was inverted, and biomass maps were computed for the individual model. The resultant biomass maps were validated using a validation set of referenced measurements. Perplexity, Shannon index, entropy, Gini Simpson index, index of qualitative inversion, Reyni entropy (order 2), σ HV , alpha, σ V V , and volumetric scattering exhibited a significant correlation between field biomass and predicted biomass computed using developed model. R 2 for validation ranges from 0.95 to 0.81 with RMSE ranging from 13.59 Mgha −1 to 25.63 Mgha −1. The estimated biomass in study site ranges from 49.31 Mgha −1 to 290.60 Mgha −1 .

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Section: Resultssupporting

confidence: 87%

Retrieval of Tropical Peatland Forest Biomass From Polarimetric Features in Central Kalimantan, Indonesia

Waqar¹,

Sukmawati²,

Ji³

et al. 2020

PIER C

View full text Add to dashboard Cite

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“…For instance, the ratio vegetation index (RVI) is effective for retrieval of the mangrove structure [48]. The normalized difference vegetation index (NDVI) has been widely used for mangrove AGB as it is strongly correlated with mangrove biophysical parameters [49]. Further, enhanced vegetation index-2 (EVI-2) is relatively sensitive to mangrove AGB [50,51].…”

Section: Image Transformation Of the S-2 Multispectral And Alos-2 Palmentioning

confidence: 99%

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

et al. 2020

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This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R 2 ) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R 2 = 0.683, RMSE = 25.08 Mg·ha −1 ) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha −1 to 142 Mg·ha −1 (with an average of 72.47 Mg·ha −1 ). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.

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“…For example, reported studies suggested that improved spatial resolution from the Sentinel series helped to reduce the uncertainty, and improved the accuracy of AGB mapping at finer scale [52,53]. Promising results demonstrated that texture measurement of SAR data with higher spatial resolution could improve biomass estimation [54,55]. However, uncertainties in implementing the texture measurement for biomass estimation were reported, due to the selection of window size [56,57].…”

Section: Satellite Data Pre-processing and Derived Variablesmentioning

confidence: 99%

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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Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

Cited by 58 publications

References 57 publications

Retrieval of Tropical Peatland Forest Biomass From Polarimetric Features in Central Kalimantan, Indonesia

Retrieval of Tropical Peatland Forest Biomass From Polarimetric Features in Central Kalimantan, Indonesia

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

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

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