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Evaluation and Optimization of Regional River System Forest Landscape Pattern in Forest Urban Agglomeration of Pearl River Delta
Received date: 2023-01-11
Revised date: 2023-04-12
Online published: 2023-06-26
To carry out forest landscape evaluation and optimization of regional river system in the Pearl River Delta forest urban agglomeration is to provide the oretical basis for forest quality improvement and high-quality development of the Pearl River Delta forest urban agglomeration.Based on the data of "one map" of forest resources management in Guangdong Province in 2021,the landscape classification and landscape pattern analysis were researched in accordance with the land type,dominant tree species,age and density.Among them,the dominant species were divided into 6 categories:eucalyptus,broad-leaved mixed forest,other broad-leaved forest,Chinese fir,coniferous and broad-leaved mixed forest,and coniferous mixed forest.The results showed that the regional river system forest landscape in the Pearl River Delta forest urban agglomerations was rich in diversity,medium in evenness and connectivity,and the forest landscape patches were simple in shape,mainly small patches,and the patch fragmentation degree was high.The forest landscape was dominated by other broad-leaved forests,accounting for 40.77%,followed by eucalyptus forest,accounting for 24.61%,and the canopy density was mainly medium forest.The ratio of young forest,medium strong forest and mature forest was 1∶2∶1.Through forest quality improvement projects such as high-quality water conservation forest construction,low-yield and low-efficiency forest transformation,and middle and young forest tending,tree species,age group and density structure were adjusted to further optimize the evenness and connectivity of forest landscape.
Zhuohong XIE , Lijie LIU , Yanqing MO , Chumin CHEN , Zhenhuan MA , Ping LIU . Evaluation and Optimization of Regional River System Forest Landscape Pattern in Forest Urban Agglomeration of Pearl River Delta[J]. Forest and Grassland Resources Research, 2023 , 0(2) : 118 -125 . DOI: 10.13466/j.cnki.lyzygl.2023.02.016
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