# Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
###### Abstract  
Ecological forests are an important part of terrestrial ecosystems, are an important carbon sink and play a pivotal role in the global carbon cycle. At present, the comprehensive utilization of optical and radar data has broad application prospects in forest parameter extraction and biomass estimation. In this study, tree and topographic data of 354 plots in key nature reserves of Liaoning Province were used for biomass analysis. Remote sensing parameters were extracted from Landsat 8 OLI and Sentinel-1A radar data. Based on the strong correlation factors obtained via Pearson correlation analysis, a linear model, BP neural network model and PSO neural network model were used to simulate the biomass of the study area. The advantages of the three models were compared and analyzed, and the optimal model was selected to invert the biomass of Liaoning province. The results showed that 44 factors were correlated with forest biomass (\(p<0.05\)), and 21 factors were significantly correlated with forest biomass (\(p<0.01\)). The comparison between the prediction results of the three models and the real results shows that the PSO-improved neural network simulation results are the best, and the coefficient of determination is 0.7657. Through analysis, it is found that there is a nonlinear relationship between actual biomass and remote sensing data. Particle swarm optimization (PSO) can effectively solve the problem of low accuracy in traditional BP neural network models while maintaining a good training speed. The improved particle swarm model has good accuracy and speed and has broad application prospects in forest biomass inversion. forest biomass; Landsat 8; Sentinel-1A; BP neural network; PSO +
Footnote †: journal: Sensors  
2023  
233931  
technology has many advantages in quantifying and mapping forest structures and in monitoring and mapping above-ground biomass, and it is accurate in both time and space. Therefore, a good biomass dataset containing canopy height and canopy structure can provide carbon sequestration potential for forest stocks [6]. Landsat 8 data are one of the most widely used remote sensing data types in biomass estimation. However, due to the short wavelength of the spectrum used, only forest canopy structure information can be obtained, and vertical direction parameters cannot be effectively obtained [7]. In addition, Landsat is prone to supersaturation when estimating high biomass forests [8]. Microwave remote sensing technology can obtain ground information effectively. Microwave penetration is strong, can penetrate the canopy and works all day. Synthetic aperture radar (SAR) is the most widely used radar in microwave remote sensing. SAR technology has the advantages of high spatial and temporal resolution, being all-weather, having no cloud interference and a strong penetrating ability in estimating forest above-ground biomass. This method has great potential and broad application prospects in forest biomass estimation [9]. However, different wavelengths of SAR have different saturation points. The longer the wavelength, the more sensitive it is to biomass, so long-wave radar data are more suitable for biomass estimation. Due to the high cost of large-area data acquisition with most long-wave SARs, their application in regional forest biomass estimation is limited. In 2013, the European Space Agency's Sentinel-1A satellite began providing free high-resolution SAR data to the world. This solved the problem perfectly. Therefore, the collaborative strategy of active and passive remote sensing data can improve the accuracy of remote sensing estimations of forest above-ground biomass [10]. The application of multi-source remote sensing data fusion can better complement each other and reduce data redundancy. With the deepening of research on remote sensing technology, the collaborative application of multi-sensor remote sensing data has gradually become a trend in forest biomass estimation; in particular, the joint use of optical and radar data has become more and more popular [11].