With　the　rapid　development　of　Unmanned　Aerial　Vehicle　(UAV)　technology,　more　and　more　UAVs　have　been　used　in　forest　survey.　UAV　(RGB)　images　are　the　most　widely　used　UAV　data　source　in　forest　resource　management.　However,　there　is　some　uncertainty　as　to　the　reliability　of　these　data　when　monitoring　height　and　growth　changes　of　low-growing　saplings　in　an　afforestation　plot　via　UAV　RGB　images.　This　study　focuses　on　an　artificial　Chinese　fir　(Cunninghamia　lancelota,　named　as　Chinese　Fir)　young　forest　plot　in　Fujian,　China.　Divide-and-conquer　(DAC)　and　the　local　maximum　(LM)　method　for　extracting　seedling　height　are　described　in　the　paper,　and　the　possibility　of　monitoring　young　forest　growth　based　on　low-cost　UAV　remote　sensing　images　was　explored.　Two　key　algorithms　were　adopted　and　compared　to　extract　the　tree　height　and　how　it　affects　the　young　forest　at　single-tree　level　from　multi-temporal　UAV　RGB　images　from　2019　to　2021.　Compared　to　field　survey　data,　the　R2　of　single　saplings’　height　extracted　from　digital　orthophoto　map　(DOM)　images　of　tree　pits　and　original　DSM　information　using　a　divide-and-conquer　method　reached　0.8577　in　2020　and　0.9968　in　2021,　respectively.　The　RMSE　reached　0.2141　in　2020　and　0.1609　in　2021.　The　R2　of　tree　height　extracted　from　the　canopy　height　model　(CHM)　via　the　LM　method　was　0.9462.　The　RMSE　was　0.3354　in　2021.　The　results　demonstrated　that　the　survival　rates　of　the　young　forest　in　the　second　year　and　the　third　year　were　99.9%　and　85.6%,　respectively.　This　study　shows　that　UAV　RGB　images　can　obtain　the　height　of　low　sapling　trees　through　a　computer　algorithm　based　on　using　3D　point　cloud　data　derived　from　high-precision　UAV　images　and　can　monitor　the　growth　of　individual　trees　combined　with　multi-stage　UAV　RGB　images　after　afforestation.　This　research　provides　a　fully　automated　method　for　evaluating　the　afforestation　results　provided　by　UAV　RGB　images.　In　the　future,　the　universality　of　the　method　should　be　evaluated　in　more　afforestation　plots　featuring　different　tree　species　and　terrain.　©　2023　by　the　authors.