Tree　height　is　an　important　vegetative　structural　parameter,　and　its　accurate　estimation　is　of　significant　ecological　and　commercial　value.　We　collected　UAV　images　of　six　tree　species　distributed　throughout　a　subtropical　campus　during　three　periods　from　March　to　late　May,　during　which　some　deciduous　trees　shed　all　of　their　leaves　and　then　regrew,　while　other　evergreen　trees　kept　some　of　their　leaves.　The　UAV　imagery　was　processed　by　computer　vision　and　photogrammetric　software　to　generate　a　three-dimensional　dense　point　cloud.　Individual　tree　height　information　extracted　from　the　dense　photogrammetric　point　cloud　was　validated　against　the　manually　measured　reference　data.　We　found　that　the　number　of　leaves　in　the　canopy　affected　tree　height　estimation,　especially　for　deciduous　trees.　During　leaf-off　conditions　or　the　early　season,　when　leaves　were　absent　or　sparse,　it　was　difficult　to　reconstruct　the　3D　canopy　structure　fully　from　the　UAV　images,　thus　resulting　in　the　underestimation　of　tree　height;　the　accuracy　improved　considerably　when　there　were　more　leaves.　For　Terminalia　mantaly　and　Ficus　virens,　the　root　mean　square　errors　(RMSEs)　of　tree　height　estimation　reduced　from　2.894　and　1.433　m　(leaf-off)　to　0.729　and　0.597　m　(leaf-on),　respectively.　We　provide　direct　evidence　that　leaf-on　conditions　have　a　positive　effect　on　tree　height　measurements　derived　from　UAV　photogrammetric　point　clouds.　This　finding　has　important　implications　for　forest　monitoring,　management,　and　change　detection　analysis.