At　the　present　stage,　the　effective　coupling　information　of　＂ground-space＂　is　still　a　fundamental　way　to　detect　forest　pest　damage　rapidly　and　accurately　in　remote　sensing.　It　is　of　great　significance　to　explore　and　construct　a　detection　model　that　can　comprehensively　and　effectively　utilize　ground　microscopic　features　and　remote　sensing　pixel　information.　Taking　Dendrolimus　punctatus　Walker　damage　as　the　study　object,　the　host　characteristics　and　the　differences　with　healthy　pine　forest　are　analyzed　from　the　aspects　of　leaf　volume,　greenness,　moisture,　forest　form　and　spectrum.　Four　experimental　sites　of　Sanming　City,　Jiangle　County,　Sha　County　and　Yanping　District　in　Nanping　City,　Fujian,　China　are　set　as　the　experimental　areas,　and　two　forest　stand　features　of　LAI　and　SEL　are　measured,　the　remote　sensing　indicators　of　NDVI,　WET　and　B-2,　B-3,　B-4　are　calculated　or　extracted.　The　PCA-RF　detection　model　is　constructed　with　pest　levels　of　non-damage,　mild　damage,　moderate　damage　and　severe　damage　as　dependent　variables.　This　model　reduces　the　dimensions　by　converting　the　initial　variables　into　several　principal　components　and　making　the　principal　components　input　to　the　random　forest.　The　first　three　principal　components　of　PCA　can　better　replace　the　information　of　the　original　seven　characteristic　indicators,　thereby　reducing　the　seven-dimensional　information　to　three　dimensions.　One　hundred　eighty-two　samples　are　randomly　divided　into　the　training　set　and　test　set　for　the　five　repeats.　The　detection　accuracy,　Kappa　coefficient,　ROC　are　selected　to　evaluate　the　pest　damage　detection　effects　of　PCA-RF　model,　and　compare　with　Fisher　discriminant　analysis　and　BP　neural　network.　The　results　show　that　the　detection　accuracy,　Kappa　coefficient　and　AUC　of　PCA-RF　model　in　the　training　set　are　not　as　good　as　those　of　FDA　and　BPNN,　but　the　detection　accuracy　and　Kappa　coefficient　in　the　test　set　are　significantly　better　than　the　other　two　algorithms,　and　the　AUC　value　is　also　significantly　higher　than　BNPP.　This　study　proves　that　PCA-RF　model　simplifies　the　complex　problem,　inherits　the　advantages　of　random　forest,　has　very　strong　generalization　ability　and　robustness,　and　can　be　used　as　an　effective　solution　for　forest　pest　and　disease　detection.