Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data

Zhao, Panpan; Lu, Dengsheng; Wang, Guangxing; Liu, Lijuan; Li, Dengqiu; Zhu, Jinru; Yu, Shu

doi:10.1016/j.jag.2016.08.007

Cited by 104 publications

(107 citation statements)

References 41 publications

Supporting

Mentioning

100

Contrasting

Order By: Relevance

“…This result is in agreement with the findings reported in References [94][95][96][97], which concluded that SVR consistently outperformed other machine learning methods. The performance (R 2 of 0.73 and RMSE of 38.68 Mg·ha −1 , Table 7) of the SVR model with the combination data of the Sentinel-2A and the ALOS-2 PALSAR-2 indicates a satisfactory result compared to previous studies on the forest AGB, as seen in Reference [98] (R 2 = 0.46), (R 2 = 0.28-0.44) [21], and (R 2 = 0.46) [99].…”

Section: Discussionmentioning

confidence: 50%

“…This study addresses key issues by investigating the combination of remote sensing data of the Sentinel-2A and the ALOS-2 PALSAR-2 for estimating the forest AGB. However, the combinations of optical images (i.e., Landsat) and SAR images (i.e., JERS-1 SAR) for the forest AGB estimation are not new [21]. Yet, to the best of our knowledge, investigating the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 for the estimation of forest ABG has seldom been carried out.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 50%

Section: Discussionmentioning

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

View full text Add to dashboard Cite

show abstract

“…31 Combining SAR and optical data has brought accurate AGB predictions in a number of cases because the structural SAR information can be complemented with canopy density, forest type, and foliage-related information collected by optical instruments. [32][33][34][35][36] Even with the increase in new data, fusion techniques, and innovative researches, AGB prediction based on SAR remains a challenging task, as saturation of the signal is common in forests, 37 where the backscatter is influenced by several factors, such as the soil moisture, especially when the vegetation coverage is low; 38 the forest type, the leaf presence with needleleaf forests possibly having the highest saturation level; 39 the seasonal and weather conditions; 40 the canopy roughness; 41 and the SAR signal polarization as well as the incidence angle. 42 Therefore, studies clarifying SAR response in different conditions and forests types are important, as they can support the effective exploitation of new SAR data streams into ecological research and monitoring.…”

Section: Introductionmentioning

confidence: 99%

Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

Laurin

Balling

Corona³

et al. 2018

J. Appl. Rem. Sens.

123

View full text Add to dashboard Cite

Abstract. The objective of this research is to test Sentinel-1 SAR multitemporal data, supported by multispectral and SAR data at other wavelengths, for fine-scale mapping of above-ground biomass (AGB) at the provincial level in a Mediterranean forested landscape. The regression results indicate good accuracy of prediction (R 2 ¼ 0.7) using integrated sensors when an upper bound of 400 Mg ha −1 is used in modeling. Multitemporal SAR information was relevant, allowing the selection of optimal Sentinel-1 data, as broadleaf forests showed a different response in backscatter throughout the year. Similar accuracy in predictions was obtained when using SAR multifrequency data or joint SAR and optical data. Predictions based on SAR data were more conservative, and in line with those from an independent sample from the National Forest Inventory, than those based on joint data types. The potential of S1 data in predicting AGB can possibly be improved if models are developed per specific groups (deciduous or evergreen species) or forest types and using a larger range of ground data. Overall, this research shows the usefulness of Sentinel-1 data to map biomass at very high resolution for local study and at considerable carbon density. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

“…In this research, the satellite remote sensing image used to estimate the carbon stocks were Landsat time series of medium resolution data with a spatial resolution of 30 × 30 m, and there were many disadvantages compared to the high-resolution satellite data used to estimate forest AGC [80]. Previous studies showed that the combination with multisource remote sensing data could effectively improve the estimation accuracy of AGC [81]. Remote sensing data were affected by their own spectral resolution, resulting in differences in the extraction accuracy of the band spectrum, vegetation index, texture information compared with high-resolution images [37,38,42,81].…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies showed that the combination with multisource remote sensing data could effectively improve the estimation accuracy of AGC [81]. Remote sensing data were affected by their own spectral resolution, resulting in differences in the extraction accuracy of the band spectrum, vegetation index, texture information compared with high-resolution images [37,38,42,81]. In addition, due to the large span of date and time acquisition of the eight scenes in the same period, although atmospheric correction was performed, radiation differences could not be completely eliminated, resulting in errors in bamboo forest information extraction [82].…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

Han

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

China is one of the countries with the most abundant bamboo forest resources in the world, and Zhejiang province is among the top-3 Chinese provinces with richest bamboo forests. For rational bamboo forests management, it is of great significance to study the spatiotemporal dynamic changes of Aboveground Carbon (AGC) stocks of bamboo forest in Zhejiang. In this study, remote sensing variables, such as spectral, vegetation indices and texture features of bamboo forest in Zhejiang, were extracted from 32 Landsat TM and OLI images got from four different years (2000, 2004, 2008 and 2014). These variables were subsequently selected with stepwise regression method to build an estimation model of AGC of the bamboo forests. The results showed that (1) the accuracy of bamboo forest remote sensing information extracted from the four different years was high with a classification accuracy of >76.26% and an accuracy of users of >91.62%. The classification area of bamboo forest was highly consistent with the area from forest resource inventory, and the area accuracy was over 96.50%; (2) the estimation model performed well in predicting the AGC in Zhejiang for different years. The correlation coefficient for estimated and measured AGC was between 63% and 72% with low root mean square error; (3) the derived AGC of the bamboo forests in Zhejiang province increased gradually from 2000 to 2014, with the AGC density of 6.75 Mg·ha −1 , 10.95 Mg·ha −1 , 15.25 Mg·ha −1 and 19.07 Mg·ha −1 respectively, and the average annual growth of 0.88 Mg·ha −1 . The spatiotemporal evolution of bamboo forest AGC in Zhejiang province had a close relationship with the gradual expansion of bamboo forest in the province and the differentiation of management levels in different regions.

show abstract

Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data

Cited by 104 publications

References 41 publications

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)

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)

Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

Contact Info

Product

Resources

About