基于林分潜在生长量的立地质量评价方法与应用

doi:10.11707/j.1001-7488.20181213

Abstract

Abstract: [Objective] Site quality assessment is fundamental to forest management and important for understanding forest growth process, estimating forest growth and yield and making silviculture practices. However, most of studies focused on site index for site quality assessment and cannot predict potential site productivity because they used realized forest inventory data. The objective of this study is to develop a methodology for site quality assessment based on basal area- or volume-related potential increment.[Method] This study gave the definition of potential site productivity and developed the models for estimating potential and realized site productivity. The procedure for site quality assessment was put forward which included site unit-stand unit-stand growth type-potential and realized productivity models-mapping site quality at sub-compartment level. A case study for mixed conifer-broad-leaved forests was carried out with the application of the methodology and national forest inventory data in Jilin Province.[Result] Site factors affecting site productivity mainly included elevation, aspect, slope position, slope, soil type, and humus depth, respectively. Site types were classified as 5 site classes through stand height growth grouping with the constraints of these site factors. Growth models of stand mean height, basal area and volume were developed by site classes with biological and statistical reasonability. The coefficients of determination were 0.991 8, 0.986 6 and 0.984 2, respectively. The potential productivity varied from 3.6 to 5.8 m³·hm^-2a^-1. The optimal stand density at potential productivity was also estimated.[Conclusion] The methodology for potential site productivity estimation is applicable for both pure and mixed forest which could predict the maximum mean annual increment at specific sites and improve the realized productivity by stand density control. It is expected to be as a reference for forest quality improvement.

Key words: site quality, potential mean annual increment, site class, stand growth type, mixed forest

CLC Number:

S757

Lei Xiangdong, Fu Liyong, Li Haikui, Li Yutang, Tang Shouzheng. Methodology and Applications of Site Quality Assessment Based on Potential Mean Annual Increment[J]. Scientia Silvae Sinicae, 2018, 54(12): 116-126.

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Methodology and Applications of Site Quality Assessment Based on Potential Mean Annual Increment

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