Estimating aboveground biomass of broadleaf, needleleaf, and mixed forests in Northeastern China through analysis of 25-m ALOS/PALSAR mosaic data

Ma, Jun; Xiao, Xiangming; Qin, Yuanwei; Chen, Bangqian; Hu, Yaoguang; Li, Xiangping; Zhao, Bin

doi:10.1016/j.foreco.2016.12.020

Cited by 32 publications

(17 citation statements)

References 71 publications

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“…SAR data can penetrate dense forests and obtain the vertical structure information of forests, so the PALSAR HH and HV backscatter coefficients and their derivative variables (sum, difference, ratio) were correlated with forest biomass. In addition, correlations between the forest biomass and HV backscatter coefficients of different forest types were higher than those between the forest biomass and HH backscatter coefficients, which is in line with previous research results [22,48]. All these factors can be considered potential variables for forest biomass estimation.…”

Section: Univariate Correlation Analysissupporting

confidence: 90%

“…According to the biomass characteristics of different forest types, it is very important to select variables with a high importance to the model [48]. The forest inventory factors selected in this study included not only the DBH and height, which are the two most relevant factors to biomass [38,39,50] but also the mean age and canopy density, which have received increasing attention in recent studies.…”

Section: Forest Biomass Estimation Model Based On Forest Inventory Anmentioning

confidence: 99%

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Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Zhu

Zhong-ke

et al. 2020

Forests

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Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.

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Section: Univariate Correlation Analysissupporting

confidence: 90%

Section: Forest Biomass Estimation Model Based On Forest Inventory Anmentioning

confidence: 99%

Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Zhu

Zhong-ke

et al. 2020

Forests

View full text Add to dashboard Cite

show abstract

“…A number of studies have reported that PALSAR backscattering coefficients are sensitive to tree structure and can be used to generate annual maps of forests 12,19,27,28 and to estimate forest above-ground biomass in different climate regions 46,47,48 .…”

Section: Palsar Datamentioning

confidence: 99%

Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017

et al. 2019

Self Cite

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“…The Changbai Mountains Mixed forests, as the richest eco-region in temperate forests of northeastern China, play a key role in carbon cycles and ecosystem services both at regional and global scales [42][43][44][45]. Hence, in this study, we innovatively developed a SVRK model based on limited samples and open-access satellite predictors, and adopted it to map stand volume of the Changbai Mountains Mixed forests, a vital eco-region of temperate ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

Chen

Ren

Zhang

et al. 2020

Forests

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Quantifying stand volume through open-access satellite remote sensing data supports proper management of forest stand. Because of limitations on single sensor and support vector machine for regression (SVR) as well as benefits from hybrid models, this study innovatively builds a hybrid model as support vector machine for regression kriging (SVRK) to map stand volume of the Changbai Mountains mixed forests covering 171,450 ha area based on a small training dataset (n = 928). This SVRK model integrated SVR and its residuals interpolated by ordinary kriging. To determine the importance of multi-sensor predictors from ALOS and Sentinel series, the increase in root mean square error (RMSE) of SVR was calculated by removing the variable after the standardization. The SVRK model achieved accuracy with mean error, RMSE and correlation coefficient in -2.67%, 25.30% and 0.76, respectively, based on an independent dataset (n = 464). The SVRK improved the accuracy of 9% than SVR based on RMSE values. Topographic indices from L band InSAR, backscatters of L band SAR, and texture features of VV channel from C band SAR, as well as vegetation indices of the optical sensor were contributive to explain spatial variations of stand volume. This study concluded that SVRK was a promising approach for mapping stand volume in the heterogeneous temperate forests with limited samples.

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Estimating aboveground biomass of broadleaf, needleleaf, and mixed forests in Northeastern China through analysis of 25-m ALOS/PALSAR mosaic data

Cited by 32 publications

References 71 publications

Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017

Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

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