Potential value of combining ALOS PALSAR and Landsat-derived tree cover data for forest biomass retrieval in Madagascar

Minh, Dinh Ho Tong; Ndikumana, Emile; Vieilledent, Ghislain; McKey, Doyle; Baghdadi, Nicolas

doi:10.1016/j.rse.2018.04.056

Cited by 31 publications

(21 citation statements)

References 27 publications

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“…This situation is different from forest applications, where the forest age is challenged to retrieval. For example, for forest biomass estimations, many works have to use prior knowledge (e.g., Landsat-derived tree cover [48]) or advanced techniques (e.g., SAR tomography [49,50]) to mitigate the radar signal saturated problem. Finally, we note that outside the European zones, there is a limitation for Sentinel-1 data since the revisit period will be 12 days.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France

et al. 2018

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The research and improvement of methods to be used for crop monitoring are currently major challenges, especially for radar images due to their speckle noise nature. The European Space Agency’s (ESA) Sentinel-1 constellation provides synthetic aperture radar (SAR) images coverage with a 6-day revisit period at a high spatial resolution of pixel spacing of 20 m. Sentinel-1 data are considerably useful, as they provide valuable information of the vegetation cover. The objective of this work is to study the capabilities of multitemporal radar images for rice height and dry biomass retrievals using Sentinel-1 data. To do this, we train Sentinel-1 data against ground measurements with classical machine learning techniques (Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Random Forest (RF)) to estimate rice height and dry biomass. The study is carried out on a multitemporal Sentinel-1 dataset acquired from May 2017 to September 2017 over the Camargue region, southern France. The ground in-situ measurements were made in the same period to collect rice height and dry biomass over 11 rice fields. The images were processed in order to produce a radar stack in C-band including dual-polarization VV (Vertical receive and Vertical transmit) and VH (Vertical receive and Horizontal transmit) data. We found that non-parametric methods (SVR and RF) had a better performance over the parametric MLR method for rice biophysical parameter retrievals. The accuracy of rice height estimation showed that rice height retrieval was strongly correlated to the in-situ rice height from dual-polarization, in which Random Forest yielded the best performance with correlation coefficient R 2 = 0.92 and the root mean square error (RMSE) 16% (7.9 cm). In addition, we demonstrated that the correlation of Sentinel-1 signal to the biomass was also very high in VH polarization with R 2 = 0.9 and RMSE = 18% (162 g·m − 2 ) (with Random Forest method). Such results indicate that the highly qualified Sentinel-1 radar data could be well exploited for rice biomass and height retrieval and they could be used for operational tasks.

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Section: Discussionmentioning

confidence: 99%

Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France

et al. 2018

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“…The Sentinel-2 Level 1C data were top-of-atmosphere reflectance, and they were processed for orthorectification and registration [38]. The MSI data had 13 spectral bands and were in 10 m (bands 2-4, 8), 20 m (band 5-7, 8a, [11][12], and 60 m (band 1, 9-10) spatial resolutions, respectively [38]. SRTM DEM data at a 30 m resolution were obtained from USGS were acquired (https://earthexplorer.usgs.gov/).…”

Section: Satellite Data Pre-processing and Derived Variablesmentioning

confidence: 99%

“…The spatial distribution of forest AGB remains inadequately quantified with certain uncertainty, especially when AGB values are higher than 150 Mg•ha −1 or lower than 40 Mg•ha −1 , with large trees and tropical issues [8,9]. This is particularly true when considering practical difficulties in inventory over broad geographic scales and complexity of the forest ecosystems [10][11][12]. Therefore, accurate estimation and rapid monitoring of forest AGB is recognized as a research challenge.…”

Section: Introductionmentioning

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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“…HH polarized data are sensitive to moisture beneath the canopy, whereas HV polarized data are sensitive to volume scattering and biomass (Hess et al 1995). However, the estimation of forest biomass using ALOS PALSAR data currently has limitations, because the L-band saturates at about 150 t/ha (Minh et al 2018). Mermoz et al found a negative correlation between the SAR backscattering coe cient and forest biomass after reaching a maximum value (Mermoz et al 2015).…”

Section: Variable Selection and Agb Estimation Accuracymentioning

confidence: 99%

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

Shen

Wang³

et al. 2020

Preprint

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Background: Aboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans. Methods: Here, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002, and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China. Results: The proposed model provided excellent performance for mapping AGB using spectral, textural, and topographical variables, and the radar backscatter coefficients. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 (t/ha), the mean absolute error ranged from 6.54 to 32.32 t/ha, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The highest coefficient of determination (0.81) and the lowest mean absolute error (6.54 t/ha) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area. Conclusions: Validation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

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Potential value of combining ALOS PALSAR and Landsat-derived tree cover data for forest biomass retrieval in Madagascar

Cited by 31 publications

References 27 publications

Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France

Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France

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

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

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