Effectively　monitoring　Pantana　phyllostachysae　Chao　(PPC)　is　essential　for　the　sustainable　development　of　the　bamboo　industry.　However,　the　morphological　similarity　between　damaged　and　off-year　bamboo　imposes　challenges　in　the　monitoring.　The　knowledge　on　whether　the　severity　of　this　pest　could　be　effectively　monitored　by　using　remote　sensing　methods　is　very　limited.　To　fill　this　gap,　this　study　aimed　to　identify　the　PPC　damage　of　moso　bamboo　leaves　using　hyperspectral　data.　Specifically,　we　investigated　differences　in　relative　chlorophyll　content　(RCC),　leaf　water　content　(LWC),　leaf　nitrogen　content　(LNC),　and　hyperspectral　spectrum　among　healthy,　damaged　(mildly　damage,　moderately　damage,　severely　damage),　and　off-year　bamboo　leaves.　Then,　the　hyperspectral　indices　sensitive　to　pest　damage　were　selected　by　recursive　feature　elimination　(RFE).　The　PPC　damage　identification　model　was　constructed　using　the　light　gradient　boosting　machine　(LightGBM)　algorithm.　We　designed　two　different　scenarios,　without　(A)　and　with　(B)　off-year　samples,　to　evaluate　the　impact　of　off-year　leaves　on　identification　results.　The　RCC,　the　LWC,　and　the　LNC　of　damaged　leaves　generally　showed　clear　declined　trends　with　the　deterioration　of　damaged　severity.　The　RCC　and　the　LNC　of　off-year　leaves　were　significantly　lower　than　those　of　healthy　and　damaged　leaves,　whereas　the　LWC　of　off-leaves　was　significantly　different　from　that　of　damaged　leaves.　The　pest　infestation　caused　noticeable　distortion　of　leaf　spectrum,　increases　in　red　and　shortwave　infrared　bands,　and　decreases　in　green　and　near-infrared　bands.　The　magnitude　of　reflectance　change　increased　with　the　pest　severity.　The　reflectance　of　off-year　leaves　in　visible　and　near-infrared　regions　was　distinguishably　higher　than　that　of　healthy　and　damaged　leaves.　The　overall　accuracy　(OA)　of　the　constructed　model　for　the　identification　of　leaves　with　different　degrees　of　damage　severity　reached　81.51%.　When　off-year,　healthy,　and　damaged　leaves　were　lumped　together,　the　OA　of　the　constructed　model　decreased　by　5%.　About　half　of　the　off-year　leaf　samples　were　misclassified　into　the　damaged　group.　The　identification　of　off-year　leaves　is　a　challenge　for　monitoring　PPC　damage　using　hyperspectral　data.　These　results　can　provide　practical　guidance　for　monitoring　PPC　using　remote　sensing　methods.