Modelling Forest Aboveground Biomass Based on GF-3 Dual-Polarized and WorldView-3 Data: A Case Study in Datong National Wetland Park, China

Wang, Guisheng; Nan, Wang; Guo, Wenyue

doi:10.1155/2021/9925940

Cited by 4 publications

(11 citation statements)

References 52 publications

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“…However, when the penetration of optical remote sensing is insufficient, radar remote sensing can be used to obtain the canopy height of wetland vegetation [66]. Comparison with the results of related studies shows that the classification accuracy of combining the red-edge band of Sentinel-2 and radar scattering coefficients of Sentinel-1 in this paper was improved by 2-5% [67,68]. Therefore, the combination of Sentinel-1 and Sentinel-2 images is helpful to improve the accuracy of AGB inversion of P. australis vegetation.…”

Section: Discussionmentioning

confidence: 80%

Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images

et al. 2022

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Phragmites australis (P. australis) is one of the most important plant species found in wetland ecosystems, and its aboveground biomass (AGB) is a key indicator for assessing the quality or health of a wetland site. In this study, we combined Sentinel-1/2 images and field observation data collected in 2020, to delineate the distribution of P. australis in the Momoge Ramsar Wetland site by using a random forest method, and further, to estimate AGB by comparing multiple linear regression models. The results showed that the overall classification accuracy of P. australis using the random forest method was 89.13% and the P. australis area in the site was 135.74 km2 in 2020. Among various remote sensing variables, the largest correlation coefficient was observed between dry weight of AGB of P. australis and Sentinel-2 red edge B7, and between fresh weight of P. australis AGB and red edge B5. The optimal models for estimating dry and fresh weight of P. australis AGB were multiple linear regression models, with an accuracy of 75.4% and 69.2%, respectively. In 2020, it was estimated that the total fresh weight of P. australis AGB in this Ramsar site was 21.2 × 107 kg and the total dry weight was 7.2 × 107 kg. The larger weight of P. australis AGB was identified mainly at central and western sites. The application of Sentinel-2 red-edge band for AGB estimation can significantly improve the model estimation accuracy. The findings of this study will provide a scientific basis for the management and protection of wetland ecosystems and sustainable utilization of P. australis resources.

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Section: Discussionmentioning

confidence: 80%

Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images

et al. 2022

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“…Canopy height is a very important input to modeling global, national, and local forest productivity dynamics including aboveground biomass (AGB), carbon, and volume as they are allometrically related [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Multi-temporal canopy height measurements can be compared to identify areas where the forest canopy has been significantly altered or removed.…”

Section: Introductionmentioning

confidence: 99%

Generating Wall-to-Wall Canopy Height Information from Discrete Data Provided by Spaceborne LiDAR System

Doyog,

Lin

2024

Forests

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Provision of multi-temporal wall-to-wall canopy height information is one of the initiatives to combat deforestation and is necessary in strategizing forest conversion and reforestation initiatives. This study generated wall-to-wall canopy height information of the subtropical forest of Lishan, Taiwan, using discrete data provided by spaceborne LiDARs, wall-to-wall passive and active remote sensing imageries, topographic data, and machine learning (ML) regression models such as gradient boosting (GB), k-nearest neighbor (k-NN), and random forest (RF). ICESat-2- and GEDI-based canopy height data were used as training data, and medium-resolution passive satellite image (Sentinel-2) data, active remote sensing data such as synthetic aperture radar (SAR), and topographic data were used as regressors. The ALS-based canopy height was used to validate the models’ performance using root mean square error (RMSE) and percentage RMSE (PRMSE) as validation criteria. Notably, GB displayed the highest accuracy among the regression models, followed by k-NN and then RF. Using the GEDI-based canopy height as training data, the GB model can achieve optimum accuracy with an RMSE/PRMSE of 8.00 m/31.59%, k-NN can achieve an RMSE/PRMSE of as low as 8.05 m/31.78%, and RF can achieve optimum RMSE/PRMSE of 8.16 m/32.24%. If using ICESat-2 data, GB can have an optimum RMSE/PRMSE of 13.89 m/54.86%; k-NN can have an optimum RMSE/PRMSE of 14.32 m/56.56%, while RF can achieve an RMSE/PRMSE of 14.72 m/58.14%. Additionally, integrating Sentinel-1 with Sentinel-2 data improves the accuracy of canopy height modeling. Finally, the study underlined the crucial relevance of correct canopy height estimation for sustainable forest management, as well as the potential ramifications of poor-quality projections on a variety of biological and environmental factors.

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“…Therefore, dual-polarization SAR images with C and L bands are still a compromise option for forest applications in large areas at present. In addition, a C-band Gaofen-3 (GF-3) SAR sensor, launched on 10 August 2016, is the first Chinese civilian satellite to be equipped with multi-polarized modes [11,24,25]. Recently, most applications with GF-3 SAR images mainly focused on vegetation classification and sea applications [11,[24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, a C-band Gaofen-3 (GF-3) SAR sensor, launched on 10 August 2016, is the first Chinese civilian satellite to be equipped with multi-polarized modes [11,24,25]. Recently, most applications with GF-3 SAR images mainly focused on vegetation classification and sea applications [11,[24][25][26][27]. However, previous studies rarely used GF-3 images with various polarization modes for quantitative estimating of forest parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, extracting enough features from dual-polarization SAR images is one of the key steps for analyzing the relationships between SAR images and forest GSV [11,[24][25][26][27][28][29][30]. Limiting polarization models and backscattering coefficients with different polarizations and features derived from a non-coherent covariance matrix are commonly extracted from dual-polarization SAR images [23,31].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Growing Stem Volume Using Dual-Polarization GaoFen-3 SAR Images in Evergreen Coniferous Forests

Long

Zheng

et al. 2023

Remote Sensing

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Unaffected by cloud cover and solar illumination, synthetic aperture radar (SAR) images have great capability to map forest growing stem volume (GSV) in complex biophysical environments. Up to now, c-band dual-polarization Gaofen-3 (GF-3) SAR images, acquired by the first Chinese civilian satellite equipped with multi-polarized modes, are rarely applied in mapping forest GSV. To evaluate the capability of dual-polarization GF-3 SAR images in mapping forest GSV, several proposed derived features were initially extracted by mathematical operations and applied to obtain optimal feature sets by different feature sorting methods and feature selection methods. Then, the maps of GSV in an evergreen coniferous forest were inverted by various machine learning algorithms and stacking ensemble learning methods with different strategies. The results implied that backscattering coefficients and partially proposed derived features showed high sensitivity to the forest GSV, and the saturation phenomenon also obviously occurred once the forest GSV was larger than 300 m3/ha. Furthermore, the results showed that the accuracy of the mapped GSV was significantly improved using the stacking ensemble learning methods. Using various optimal feature sets and base models (MLR, KNN, SVM, and RF), the rRMSE values mainly ranged from 30% to 40%. After using the stacking ensemble learning methods, the values of rRMSE ranged from 16.71% to 20.51%. This confirmed that dual-polarization GF-3 images have great potential to map forest GSV in evergreen coniferous forests.

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Modelling Forest Aboveground Biomass Based on GF-3 Dual-Polarized and WorldView-3 Data: A Case Study in Datong National Wetland Park, China

Cited by 4 publications

References 52 publications

Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images

Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images

Generating Wall-to-Wall Canopy Height Information from Discrete Data Provided by Spaceborne LiDAR System

Mapping Growing Stem Volume Using Dual-Polarization GaoFen-3 SAR Images in Evergreen Coniferous Forests

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