Non-destructive　methods　are　widely　used　for　field　measurement　of　leaf　area　index　(LAI).　However,　the　aboveground　woody　components　of　trees　and　shrubs,　i.e.,　trunks　and　branches,　largely　affect　the　measured　gap　fraction,　thus　hindering　the　accurate　measurement　of　LAI.　Many　efforts　have　been　made　to　correct　for　the　woody　component　effect　and　estimate　LAI,　but　there　is　a　lack　of　research　to　systematically　compare　and　analyze　current　methods,　which　mainly　include:　1)　correcting　using　woody-to-total　area　ratio　(alpha),　2)　transforming　the　leaf-on　images　into　leaf-only　images　using　artificially　selected　thresholds　to　determine　whether　woody　components　blocked　leaves　(TRM　method),　and　3)　establishing　the　regression　relationship　between　LAI　and　image　features　and/or　other　measured　parameters　using　random　forests　(RFRM　method)　or　4)　neural　networks　(NNRM　method).　We　used　rich　data　generated　from　4734　scenes　and　39　tree　species　to　compare　and　analyze　these　methods.　Additionally,　considering　the　problems　with　the　existing　methods　and　the　increasing　requirements　of　LAI　measurement,　we　proposed　a　new　method　(P2PLAI)　using　an　image-to-image　translation　neural　network　(i.e.,　Pixel2Pixel)　to　transform　the　leaf-on　images　into　leaf-only　images.　The　effective　LAI　(LAIe)　was　estimated　using　the　translated　leaf-only　images,　and　then　the　LAIe　was　converted　into　LAI　using　the　clumping　index.　The　results　showed　that　the　traditional　method　using　alpha　was　limited　by　the　accuracy　of　the　alpha　estimation,　with　the　RMSE　from　0.34　to　0.92　and　the　absolute　percentage　error　(Bias%)　from　9.56　%　to　22.29　%.　The　TRM　method　could　not　stably　and　accurately　transform　the　leaf-on　images　and　underestimated　LAI,　with　the　RMSE　from　0.13　to　0.78　and　Bias%　from　3.39　%　to　21.13　%.　The　regression　methods　(i.e.,　RFRM　and　NNRM)　had　strong　limitations　since　the　accuracy　of　these　two　methods　was　related　to　the　tree　species　and　viewing　zenith　angles　(VZAs)　with　RMSE　up　to　3.12　and　Bias%　up　to　84.74　%.　The　P2PLAI　method　achieved　the　best　agreement　with　the　reference　LAI.　The　RMSE　and　Bias%　of　P2PLAI　respectively　ranged　from　0.05　to　0.26　and　from　1.27　%　to　7.70　%　and　were　not　influenced　by　tree　species　and　VZAs.　This　study　cautions　against　applying　regression　methods　such　as　RFRM　and　NNRM　for　the　indirect　measurement　of　LAI　in　forests　due　to　the　complicated　structures　of　vegetation　components.　The　combination　of　an　image-to-image　translation　neural　network　and　a　clumping　effect　correction　model　with　physical　meaning　is　recommended　to　measure　LAI　with　digital　photography.