Estimating Forest Above-Ground Biomass in Central Amazonia Using Polarimetric Attributes of ALOS/PALSAR Images

Narvaes, Igor da Silva; Santos, João Roberto dos; Bispo, Polyanna da Conceição; Graça, Paulo Maurı́cio Lima de Alencastro; Guimarães, Ulisses Silva; Gama, Fábio Furlan

doi:10.3390/f14050941

Cited by 5 publications

(1 citation statement)

References 74 publications

(143 reference statements)

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“…Moreover, although RTC is carried out in the pre-processing of PolSAR data, it may also be worth taking slope, aspect, and local incidence angle into account in model training. In addition, more polarization parameters also contribute to the application of SAR data [67,98,99]. Through the comprehensive analysis of these multivariate parameters, we can expect a significant improvement in the predictive power of the model and a deeper understanding of the object of study.…”

Section: Potential Limitationsmentioning

confidence: 99%

Inversion of Forest Aboveground Biomass in Regions with Complex Terrain Based on PolSAR Data and a Machine Learning Model: Radiometric Terrain Correction Assessment

Nie,

Sa,

Chumachenko

et al. 2024

Remote Sensing

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The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction (RTC) process for introducing normalized correction factors, which has strong effectiveness and robustness in terms of the backscattering coefficient of polarimetric synthetic aperture radar (PolSAR) data and the monadic model. However, the impact of RTC on the correctness of feature extraction and the performance of regression models requires further exploration in the retrieval of forest AGB based on a machine learning multiple regression model. In this study, based on PolSAR data provided by ALOS-2, 117 feature variables were accurately extracted using the RTC process, and then Boruta and recursive feature elimination with cross-validation (RFECV) algorithms were used to perform multi-step feature selection. Finally, 10 machine learning regression models and the Optuna algorithm were used to evaluate the effectiveness and robustness of RTC in improving the quality of the PolSAR feature set and the performance of the regression models. The results revealed that, compared with the situation without RTC treatment, RTC can effectively and robustly improve the accuracy of PolSAR features (the Pearson correlation R between the PolSAR features and measured forest AGB increased by 0.26 on average) and the performance of regression models (the coefficient of determination R2 increased by 0.14 on average, and the rRMSE decreased by 4.20% on average), but there is a certain degree of overcorrection in the RTC process. In addition, in situations where the data exhibit linear relationships, linear models remain a powerful and practical choice due to their efficient and stable characteristics. For example, the optimal regression model in this study is the Bayesian Ridge linear regression model (R2 = 0.82, rRMSE = 18.06%).

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Section: Potential Limitationsmentioning

confidence: 99%

Inversion of Forest Aboveground Biomass in Regions with Complex Terrain Based on PolSAR Data and a Machine Learning Model: Radiometric Terrain Correction Assessment

Nie,

Sa,

Chumachenko

et al. 2024

Remote Sensing

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Feasibility of using Ku-band helicopter-borne microwave radar for stem volume and biomass estimation in boreal forest

Feng,

Yu,

Chen

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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Forest Height Inversion by Convolutional Neural Networks Based on L-Band PolInSAR Data Without Prior Knowledge Dependency

Li,

Lu,

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Forest height is a key forest parameter which is of great significance for monitoring forest resources, calculating forest biomass, and observing the global carbon cycle. Because the PolInSAR system could provide various object information including height, shape and direction sensitivity, and spatial distribution, it becomes a powerful means for measuring forest height. The proposed framework utilizes deep learning and builds upon traditional DEM differencing and coherence amplitude inversion algorithms. By using L band PolInSAR data, a new CNN model is established in which the estimated results of DEM differencing and coherence amplitude inversion are used as labels. Furthermore, the PCGrad optimization strategy is used for updating the gradient automatically in the training stage. This model could not only builds a relationship between complex coherences and forest height but also makes full use of the spatial context information by using the CNN layers. Experiments are carried out based on the simulated data and real data, named Lope forest site, which are collected by UAVSAR in the NASA AfriSAR campaign. Compared to the classic forest height inversion algorithms, the proposed framework has achieved a higher level of accuracy and performance on RMSE (10.15m) and R 2 (0.87). Overall, the proposed framework does not require LiDAR data as prior knowledge and can be performed on various forest scenes. Consequently, it will hopefully serve as a useful approach for improvements in forest height inversion based on PolInSAR data. Codes, trained model, and data will be available for public access.

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Estimating Forest Above-Ground Biomass in Central Amazonia Using Polarimetric Attributes of ALOS/PALSAR Images

Cited by 5 publications

References 74 publications

Inversion of Forest Aboveground Biomass in Regions with Complex Terrain Based on PolSAR Data and a Machine Learning Model: Radiometric Terrain Correction Assessment

Inversion of Forest Aboveground Biomass in Regions with Complex Terrain Based on PolSAR Data and a Machine Learning Model: Radiometric Terrain Correction Assessment

Feasibility of using Ku-band helicopter-borne microwave radar for stem volume and biomass estimation in boreal forest

Forest Height Inversion by Convolutional Neural Networks Based on L-Band PolInSAR Data Without Prior Knowledge Dependency

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