GA-SVR Algorithm for Improving Forest Above Ground Biomass Estimation Using SAR Data

Ji, Yongjie; Xu, Kunpeng; Zeng, Peng; Zhang, Wangfei

doi:10.1109/jstars.2021.3089151

Cited by 19 publications

(14 citation statements)

References 36 publications

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“…In test site I, SAR parameters extracted from the Cband showed better performance than those from the L-band for both total and component AGB inversion. The results were in conformity with our previous study in the same study area but where we used a different inversion model [44]. However, they differed from the results mentioned in the research of Kasischke et al and Cronin et al [13,22].…”

Section: Discussionsupporting

confidence: 89%

Forest Total and Component Above-Ground Biomass (AGB) Estimation through C- and L-band Polarimetric SAR Data

Zeng

Zhang

et al. 2022

Forests

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Forest biomass plays an essential role in forest carbon reservoir studies, biodiversity protection, forest management, and climate change mitigation actions. Synthetic Aperture Radar (SAR), especially the polarimetric SAR with the capability of identifying different aspects of forest structure, shows great potential in the accurate estimation of total and component forest above-ground biomass (AGB), including stem, bark, branch, and leaf biomass. This study aims to fully explore the potential of polarimetric parameters at the C- and L-bands to achieve high estimation accuracy and improve the estimation of AGB saturation levels. In this study, the backscattering coefficients at different polarimetric channels and polarimetric parameters extracted from Freeman2, Yamaguchi3, H-A-Alpha, and Target Scattering Vector Model (TSVM) decomposition methods were optimized by a random forest algorithm, first, and then inputted into linear regression models to estimate the total forest AGB and biomass components of two test sites in China. The results showed that polarimetric observations had great potential in total and component AGB estimation in the two test sites; the best performances were for leaves at test site I, with R2 = 0.637 and RMSE = 1.27 t/hm2. The estimation of biomass components at both test sites showed obvious saturation phenomenon estimation according to their scatter plots. The results obtained at both test sites demonstrated the potential of polarimetric parameters in total and component biomass estimation.

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Section: Discussionsupporting

confidence: 89%

Forest Total and Component Above-Ground Biomass (AGB) Estimation through C- and L-band Polarimetric SAR Data

Zeng

Zhang

et al. 2022

Forests

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“…Although several soft measurement models based on deep learning can achieve this goal, those models still have limitations. For example, in the case of a large amount of data, complex physical structures, and increased dimensions, the SVR modeling method is generally inefficient [15,16]. Also, this method only uses a shallow network for training rather than repeatedly training through a multilayer network, so it cannot extract the characteristic information in the data, which will affect the model's prediction accuracy.…”

Section: Overall Architecture Of the Proposed Methodsmentioning

confidence: 99%

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

Jiang,

Teng,

Zhao

2024

Applied Sciences

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In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) network based on system identification to accurately predict the crystal diameter. The main steps of the proposed method are as follows: First, we address the delay problem of the effects of the temperature and crystal pulling speed on the tail diameter growth by using a back propagation (BP) neural network based on the mean impact value (MIV) method to determine the optimal delay time. Second, we construct a prediction model of the tail diameter by using the DBN network with the temperature and crystal pulling speed as input variables in the crystal growth process. Third, we improve the DBN network by using the SVR network to enhance its linear regression capability. We also employ the ant colony optimization (ACO) algorithm to obtain the optimal parameters of the SVR network. Finally, we compare the performance of the DBN-ACO-SVR network based on system identification with the DBN and SVR networks, and the results show that our method can effectively deal with the delay problem and achieve the accurate prediction of the tail diameter in the Czochralski silicon single crystal growth process.

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“…These images also provide sufficient coverage for regional-scale AGB estimation [18,21,22]. The relationship between forest-related remote sensing parameters and AGB is often nonlinear, leading to the considerable use of nonparametric models in AGB estimation [23][24][25][26]. Machine learning algorithms, such as random forests (RFs), support vector regression (SVR), artificial neural networks (ANNs), and k-nearest neighbors (k-NNs), have been applied due to their effective handling of enormous datasets and the relative simplicity with which they can simulate complicated nonlinear interactions between variables [27].…”

Section: Introductionmentioning

confidence: 99%

Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

Wang

Yi²,

et al. 2023

Sustainability

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More refined and economical aboveground biomass (AGB) monitoring techniques are needed because of the growing significance of spruce plantations in climate change mitigation programs. Due to the challenges of conducting field surveys, such as the potential inaccessibility and high cost, this study proposes a convenient and efficient alternative to traditional field surveys that integrates Gaofen-2 (GF-2) satellite optical images and unmanned aerial vehicle (UAV)-acquired optical and point cloud data to provide a reliable and refined estimation of the aboveground biomass (AGB) in spruce plantations. The feasibility of using data produced from the semiautomatic processing of UAV-based images and photogrammetric point clouds to replace conventional field surveys of sample plots in a young spruce plantation was evaluated. The AGB in 53 sample plots was estimated using data extracted from the UAV imagery. The UAV plot data and GF-2 optical data were used in four regression models to estimate the AGB in the study area. The coefficient of determination (R2), root-mean-square error (RMSE), mean percent standard error (MPSE), and Lin’s concordance correlation coefficient (LCCC) were calculated through five-fold cross-validation and stratified random sampling to evaluate the models’ efficacies. In the end, the most accurate model was used to generate the spatial distribution map of the AGB. The results revealed the following: (1) the individual-tree height (R2 = 0.90) and crown diameter (R2 = 0.74) extracted from UAV data were accurate enough to replace field surveys used to obtain the AGB at the plot levels; (2) the random forest (RF) model (R2 = 0.86; RMSE = 1.75 t/ha; MPSE = 15.75%; LCCC = 0.91) outperformed the ordinary least-squares (OLS) model (R2 = 0.68; RMSE = 2.49 t/ha; MPSE = 22.94%; LCCC = 0.81), artificial neural network (ANN) model (R2 = 0.67; RMSE = 2.54 t/ha; MPSE = 21.48%; LCCC = 0.80), and support vector machine (SVM) model (R2 = 0.60; RMSE = 2.84 t/ha; MPSE = 31.73%; LCCC = 0.76) in terms of the estimation accuracy; (3) an AGB map generated by the random forest model was in good agreement with field surveys and the age of the spruce plantations. Therefore, the method proposed in this study can be used as a refined and cost-effective way to estimate the AGB in young spruce plantations.

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GA-SVR Algorithm for Improving Forest Above Ground Biomass Estimation Using SAR Data

Cited by 19 publications

References 36 publications

Forest Total and Component Above-Ground Biomass (AGB) Estimation through C- and L-band Polarimetric SAR Data

Forest Total and Component Above-Ground Biomass (AGB) Estimation through C- and L-band Polarimetric SAR Data

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

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