Prediction of air pollutant concentrations based on the long short-term memory neural network.

In recent years, environmental problems caused by air pollutants have received increasing attention. Effective prediction of air pollutant concentrations is an important way to protect the public from harm. Recently, due to extreme climate and social development, the forest fire frequency has increased. During the biomass combustion process caused by forest fires, the content of particulate matter (PM) in the atmosphere increases significantly. However, most existing air pollutant concentration prediction methods do not consider the considerable impact of forest fires, and effective long-term prediction models have not been established to provide early warnings for harmful gases. Therefore, in this paper, we collected a daily air quality data set (aerodynamic diameter smaller than 2.5 µm, PM2.5) for Heilongjiang Province, China, from 2017 to 2023 and A novel Long Short-Term Memory (LSTM) model was proposed to effectively predict the situation of air pollutants. The model could automatically extract information of the effective time step from the historical data set and combine forest fire disturbance and climate data as auxiliary data to improve the model prediction ability. Moreover, we created artificial neural network (ANN) and permissive regression (support vector machine, SVR) models for comparative experiments. The results showed that the precision accuracy of the developed LSTM model is higher. Unlike the other models, the LSTM neural network model could effectively predict the concentration of air pollutants in long-term series. Regarding long-term observation missions (7 days), the proposed model performed well and stably, with R2 reaching over 88%.

Forest age mapping based on multiple-resource remote sensing data.

Forest age is an important stand description factor and plays an important role in the carbon cycle of forest ecosystems. However, forest age data are typically lacking or are difficult to acquire at large spatial scale. Thus, it is important to develop a method of spatial forest age mapping. In this study, a method of forest age estimation based on multiple-resource remote sensing data was developed. Forest age was estimated by using average tree height estimated from the ICESat/GLAS and MODIS BRDF products. The results showed that forest age was significantly related to average tree height with a correlation coefficient of 0.752. Then, the average tree height was inversed using a waveform parameter extracted from ICESat/GLAS and was extended to continuous space with the help of the MODIS BRDF product. Thus, forest age mapping was realized. Lastly, the structure of forest age in the study area was evaluated. The results indicated that this method can be used to estimate forest age at the local scale and at large scale and can facilitate understandings of the real forest age structure features of a research area.