Non-randomness　within　canopies　and　woody　component　are　two　factors　limiting　the　accuracy　of　indirect　leaf　area　index　(LAI)　measurement.　Here　we　combine　the　path　length　distribution　model　and　Multispectral　Canopy　Imager　(MCI)　together　for　the　first　time　to　improve　the　accuracy.　The　results　show　that　non-randomness　within　canopies　underestimates　17.1%-28.2%　LAI,　while　woody　component　overestimates　14.6%-27.8%　LAI　in　four　forest　sites.　Although　these　two　factors　were　sometimes　offset,　the　degree　of　non-randomness　within　canopies　and　the　proportion　of　woody　component　vary　in　different　forests.　More　attention　should　be　paid　to　the　impact　of　the　non-randomness　within　canopies　and　the　woody　component,　especially　in　coniferous　forest　dominated　by　tree　trunks　and　branches.