With　the　progression　of　LiDAR　(Light　Detection　and　Ranging)　towards　a　mainstream　resource　management　tool,　it　has　become　necessary　to　understand　how　best　to　process　and　analyze　the　data.　While　most　ground　surface　identification　algorithms　remain　proprietary　and　have　high　purchase　costs;　a　few　are　openly　available,　free　to　use,　and　are　supported　by　published　results.　Two　of　the　latter　are　the　multiscale　curvature　classification　and　the　Boise　Center　Aerospace　Laboratory　LiDAR　(BCAL)　algorithms.　This　study　investigated　the　accuracy　of　these　two　algorithms　(and　a　combination　of　the　two)　to　create　a　digital　terrain　model　from　a　raw　LiDAR　point　cloud　in　a　semi-arid　landscape.　Accuracy　of　each　algorithm　was　assessed　via　comparison　with　＞7,000　high　precision　survey　points　stratified　across　six　different　cover　types.　The　overall　performance　of　both　algorithms　differed　by　only　2%;　however,　within　specific　cover　types　significant　differences　were　observed　in　accuracy.　The　results　highlight　the　accuracy　of　both　algorithms　across　a　variety　of　vegetation　types,　and　ultimately　suggest　specific　scenarios　where　one　approach　may　outperform　the　other.　Each　algorithm　produced　similar　results　except　in　the　ceanothus　and　conifer　cover　types　where　BCAL　produced　lower　errors.