Machine Learning Model for Revealing the Characteristics of Soil Nutrients and Aboveground Biomass of Northeast Forest, China

Abstract

Declining soil quality and climate change may affect species diversity and forest biomass productivity in many temperate regions in the future. Our research objective is to reveal the characteristics of soil nutrients and biomass of forests in Northeast China with climate change. The purpose of this study was to determine the soil physical and chemical properties of mature broad-leaved forest in the cold temperate zone of Mt. Changbai, Jilin Province, by measuring pH, NH4 +, organic matter (%), C/N, available phosphorus, alkali-hydrolysable N, rapidly available K, and Cr etc., analysing species diversity characteristics, and estimating aboveground biomass (AGB) of tree species with machine learning models. The results showed that with the increase of soil depth, the soil physical and chemical parameters have a decreasing trend; with the increase of soil depth, the soil nutrient content decreased; the main tree species were the Acer barbinerve (6937), Carpinus cordata Bl. (6682) and Acer mandshuricum Maxim. (5447) etc. The total difference (SOR) showed a similar trend in the four directions and central point; the reference sample size at central point, north, west, south and east direction was 903, 954, 971, 1005 and 1016, respectively; GRNN model was the relatively best model among these models for modelling the aboveground biomass of the trees. Therefore, the diversity of tree species in north-eastern forests was affected by soil nutrients, climate change also has a significant impact on the aboveground biomass of northeast forests, which provides a theoretical basis for the management of northeast forests about soil physical and chemical properties and species diversity.