The　clumping　effect　is　the　main　issue　causing　the　heterogeneity　in　vegetation　canopies　and　the　underestimation　of　leaf　area　index　(LAI)　obtained　using　indirect　measurement　methods.　Significant　efforts　have　been　exerted　to　correct　for　the　clumping　effect　and　derive　the　true　LAI.　Recent　research　has　shown　that　the　fractal　dimension　(FD)　is　directly　related　to　the　clumping　effect　of　foliage,　yet　practical　methods　are　needed　to　calculate　field　estimates.　Considering　that　widely　used　LAI　applications　such　as　digital　hemispherical　photography　(DHP),　tracing　radiation　and　architecture　of　canopies　(TRAC),　and　digital　cover　photography　(DCP)　estimate　LAI　with　one-dimensional　(1D)　gap　probability　and　gap　size　data,　we　propose　a　method　to　correct　for　the　clumping　effect　using　1D　FD.　Resulting　formulae　describing　the　relationship　between　LAI,　CI,　and　1D　FD　were　based　on　the　box-counting　method　(BCM)　and　a　binomial　distribution　model.　Sixty-four　simulated　scenes　including　four　RAdiation　transfer　Model　Intercomparison　(RAMI)　actual　canopies　and　field　measurements　from　nine　plots　(four　orchard　plots　and　five　coniferous　forest　plots)　were　used　to　validate　the　novel　method.　Results　showed　good　agreement　with　reference　LAI　values　for　simulated　scenes　(R2　=　0.96　and　RMSE　=　0.35).　The　1DFD　method　generated　higher　LAI　estimates　compared　with　the　LAI　measured　using　TRAC　at　the　four　orchard　plots　especially　at　high　canopy　closure,　while　its　results　were　more　consistent　with　LAI　obtained　by　litter　collection　than　those　of　comparable　methods　at　coniferous　forest　plots　(bias　from-13.5%　to　9.9%　for　DCP　images,　from-3.0%　to　19.7%　for　DHP　images,　and　from-3.8%　to　17.0%　for　TRAC　transects).　Our　validation　efforts　indicate　that　the　method　proposed　herein　corrects　for　the　clumping　effect　of　vegetated　canopies　more　effectively　with　DCP　images,　DHP　images,　and　TRAC　measurement　when　compared　with　traditional　indirect　optical　methods.　The　1DFD　method　is　expected　to　improve　indirect　measurement　accuracy　of　LAI.