立木生物量模型是森林碳储量估算与生态系统服务功能评估的基础,构建区域优势树种立木生物量模型对于提高森林碳储量和碳汇量测算准确度具有重要意义。基于重庆低山丘陵区典型针叶树种的90株实测生物量数据,构建马尾松、杉木(全株及树干、树冠、地上部分、地下部分)和柏木(地上部分及树干、树冠)的一元、二元生物量模型,并评估模型拟合精度及区域适用性。结果表明:1)构建的马尾松、杉木地上部分、地下部分和全株的二元生物量模型以及柏木地上部分二元生物量模型的决定系数(R²)均在0.930以上,平均预估误差(MPE)均在2%以下,优于《立木生物量建模方法技术规程》(LY/T 2258—2014)的精度要求。2)与《主要树种立木生物量模型与碳计量参数》(GB/T 43648—2024)中适用重庆区域的3个针叶树种生物量模型相比,构建的马尾松、杉木地上和地下部分二元生物量模型在模型精度和估测结果准确度上均有所提升,更适用于测算重庆市内的生物量。构建的生物量模型可为准确估算重庆乃至西南地区典型针叶树种林分的生物量及碳储量提供依据。

Tree biomass models are essential tools for estimating forest carbon stocks and assessing ecosystem services,and building biomass models for regionally dominant tree species is of great significance for calculating biomass and carbon storage more accurately.Based on the measured biomass data of 90 sample trees of typical coniferous tree species in low mountain and hilly region of Chongqing,this study developed single-variable and two-variable biomass models for Pinus massoniana and Cunninghamia lanceolata(including whole-tree,stem,crown,aboveground and belowground components)as well as Cupressus funebris(including aboveground biomass,stem and crown components),and the model accuracy and applicability were evaluated.The results showed that:1)The determination correlation coefficients(R²)of the two-variable biomass models for Pinus massoniana,Cunninghamia lanceolata(aboveground,belowground,and whole-tree),and the aboveground part of Cupressus funebris were all greater than 0.930.The mean prediction errors(MPE)were less than 2%,which met the precision requirements of LY/T 2258—2014.2)Compared with the three coniferous tree biomass models applicable to the Chongqing region in GB/T 43648—2024,the developed two-variable biomass models for aboveground and belowground parts of Pinus massoniana and Cunninghamia lanceolata showed improvements in both model precision and estimation accuracy,and making them more suitable for biomass calculations within Chongqing.The findings will provide valuable data support and methodological reference for regional coniferous forest carbon stock assessment and refined forest management in Chongqing and the broader Southwest China region.