Pantana　phyllostachysae　Chao　(PPC)　is　one　of　the　most　important　leaf-eating　pests　of　bamboo　forests　in　China.　It　has　become　a　major　factor　threatening　the　health　of　Moso　bamboo　forest　and　restricting　the　high　quality　and　sustainable　development　of　bamboo　industry.　It　also　has　the　characteristics　of　group-occurring,　periodicity,　and　extremely　serious　harm,　etc.　How　to　quickly　and　accurately　detect　the　damage　of　the　Moso　bamboo　forest　is　a　problem　that　needs　to　be　solved　at　this　stage.　Whereas　remote-sensing　products　can　support　the　quickly,　accurate,　and　comprehensive　monitoring　of　forest　health.　Therefore,　Sentinel-2A　MultiSpectral　Instrument　(MSI)　data,　with　three　bands　at　the　red-edge　position,　was　of　great　significance　for　pest　and　disease　detection　in　forests.　By　screening　22　spectrally　derived　indicators　(e.g.　leaf　abscission,　greenness　and　water　content)　using　ANOVA　combined　with　recursive　RFE,　totally　10　features　were　finally　obtained　to　identify　PPC　damage.　Based　on　the　above　results,　the　XGBoost　detection　model　was　established　to　detect　PCC　damage　with　high　recognition　accuracy.　The　results　showed　that　Sentinel-2A　MSI　bands　6,　7,　8,　and　8a　exhibited　strong　responses　to　PPC　damage;　the　index　constructed　by　the　red-edge　and　near-infrared　bands　effectively　reflected　the　damage　to　bamboo　forests;　the　overall　detection　accuracy　of　model　was　83.70%　compared　with　94.72%,　72.06%,　79.77%,　and　92.41%　for　＇healthy＇,　＇mildly　damaged＇,　＇moderately　damaged＇,　and　＇severely　damaged＇　categories,　respectively.　These　results　indicated　that　the　XGBoost　detection　model　provided　valuable　support　for　the　large-scale　monitoring　of　pest　damage　to　Moso　bamboo　forests.　©　2022,　Chinese　Society　of　Agricultural　Machinery.　All　right　reserved.