Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data

Basuki, Tyas Mutiara; Skidmore, Andrew K.; Hussin, Y.A.; Duren, Iris van

doi:10.1080/01431161.2013.777486

Cited by 50 publications

(40 citation statements)

References 62 publications

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“…The NDVI information added to the SAR data in the third (c) test also improved the accuracy of SAR data alone, slightly more than that done by the SAR mean texture. This indicates a complementarity of the SAR and optical datatypes, as already shown by other researches [31,[33][34][35]53], and underlines the importance of forest cover information especially when considering simultaneously different vegetation communities, as in this study. When-in test (d)-both mean NDVI and mean HH SAR textures are added to the SAR backscattering statistics, only the first input is selected by the stepwise procedure, possibly due to the redundant cover information present in both HH and NDVI datasets.…”

Section: Discussionsupporting

confidence: 50%

See 1 more Smart Citation

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

et al. 2016

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Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R 2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R 2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar.

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Section: Discussionsupporting

confidence: 50%

“…In this respect, Deng et al [33] found beneficial the addition of WorldView-2 to ALOS PALSAR in mountain Chinese forests; both Basuki et al [34] and Fedrigo et al [35] improved the ALOS PALSAR-based estimation by integrating Landsat 7 ETM+ data in tropical forests.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…In this paper, PALSAR data and three classification algorithms were explored for mapping oil palm plantations in Cameroon, a hotspot region for oil palm plantations. To compare the performance of the three different classification methods, different training sample sizes (20,50,100,200, 300, 400, 500 pixels for each type) and parameters were used to conduct the classification experiments. The results showed that a map of oil palm plantations at 50m spatial resolution can be obtained using PALSAR data with the overall accuracies and Kappa coefficients above 88% and 0.79 respectively, for a bigger training sample size (i.e., more than 500 pixels per class).…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have used PALSAR data for forest monitoring and classification [17,[20][21][22][23][24][25][26][27][28][29]. PALSAR data are often combined with optical images such as Landsat and MODIS.…”

Section: Introductionmentioning

confidence: 99%

Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images

Dong

Tenku

et al. 2015

Remote Sensing

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Oil palm plantations have expanded rapidly. Estimating either positive effects on the economy, or negative effects on the environment, requires accurate maps. In this paper, three classification algorithms (Support Vector Machine (SVM), Decision Tree and K-Means) were explored to map oil palm plantations in Cameroon, using PALSAR 50 m Orthorectified Mosaic images and differently sized training samples. SVM had the ideal performance with overall accuracy ranging from 86% to 92% and a Kappa coefficient from 0.76 to 0.85, depending upon the training sample size (ranging from 20 to 500 pixels per class). The advantage of SVM was more obvious when the training sample size was smaller. K-Means required the user's intervention, and thus, the accuracy depended on the level of his/her expertise and experience. For large-scale mapping of oil palm plantations, the Decision Tree algorithm outperformed both SVM and K-Means in terms of speed and performance. In addition, the decision threshold values of Decision Tree for a large training sample size agrees with the results from previous studies, which implies the possible universality of the decision threshold. If it can be verified, the Decision Tree algorithm will be an easy and robust methodology for mapping oil palm plantations. OPEN ACCESSRemote Sens. 2015, 7 1207

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“…The estimation of vegetation parameters (e.g., AGB, vegetation height, growing stock volume) can be improved by the fusing of SAR imagery with optical data (e.g., from Landsat) and complementary information such as altitude [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Urbazaev

Thiel

Migliavacca

et al. 2016

Forests

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Accurate estimates of aboveground biomass (AGB) are crucial to assess terrestrial C-stocks and C-emissions as well as to develop sustainable forest management strategies. In this study we used Synthetic Aperture Radar (SAR) data acquired at L-band and the Landsat tree cover product together with Moderate Resolution Image Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data to improve AGB estimations over two study areas in southern Mexico. We used Mexican National Forest Inventory (INFyS) data collected between 2005 and 2011 to calibrate AGB models as well as to validate the derived AGB products. We applied MODIS NDVI time series data analysis to exclude field plots in which abrupt changes were detected. For this, we used Breaks For Additive Seasonal and Trend analysis (BFAST). We modelled AGB using an original field dataset and BFAST-filtered data. The results show higher accuracies of AGB estimations using BFAST-filtered data than using original field data in terms of R 2 and root mean square error (RMSE) for both dry and humid tropical forests of southern Mexico. The best results were found in areas with high deforestation rates where the AGB models based on the BFAST-filtered data substantially outperformed those based on original field data (R 2 BFAST = 0.62 vs. R 2 orig = 0.45; RMSE BFAST = 28.4 t/ha vs. RMSE orig = 33.8 t/ha). We conclude that the presented method shows great potential to improve AGB estimations and can be easily and automatically implemented over large areas.

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Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data

Cited by 50 publications

References 62 publications

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

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

Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

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