Establishment of forest health indicators of Robinia pseudoacacia plantation based on UAV LiDAR

doi:10.14088/j.cnki.issn0439-8114.2022.03.032

Abstract

Abstract: LiDAR（Light detection and ranging） data of UAV was used to extract LiDAR characteristic variables that can reflect the changes of vegetation vertical and horizontal structure. Forest health indicators were constructed by correlation analysis and hierarchical clustering method to identify the health status of robinia pseudoacacia plantation in the Yellow river delta. The results showed that,the forest health indicators were composed of LAD_cv （the coefficient of variation of leaf area density）, weibull_α （the scale parameter of the Weibull density function）, H₉₉ （the percentile height of 99th） and VCI （Vertical complexity index）;Using forest health indicators to judge the health status of Robinia pseudoacacia forest could get an ideal result （total accuracy of 86.7%, Kappa coefficient of 0.79）, which confirmed the potential of LiDAR technology in forest health assessment.

Key words: forest health indicators, UAV LiDAR, Robinia pseudoacacia, the Yellow river delta

CLC Number:

MENG Peng-yu. Establishment of forest health indicators of Robinia pseudoacacia plantation based on UAV LiDAR[J]. HUBEI AGRICULTURAL SCIENCES, 2022, 61(3): 156-160.

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