The　nighttime　urban　environment　is　increasingly　affected　by　various　forms　of　artificial　light　at　night.　Color　temperature,　a　critical　characteristic　of　light,　has　significant　effects　on　numerous　fields　and　industries.　The　widespread　adoption　of　light-emitting　diode　(LED)　light,　a　low-carbon　technology,　has　resulted　in　extensive　use　of　lights　with　varying　color　temperatures　in　diverse　settings.　However,　it　is　crucial　to　recognize　that　different　color　temperatures　have　distinct　impacts　on　human　health　and　ecological　systems.　Therefore,　understanding　the　spatial　distribution　and　composition　of　nighttime　light　(NTL)　with　different　color　temperatures　is　essential　for　developing　sustainable　strategies　that　balance　public　safety,　energy　consumption,　and　ecosystem　conservation.　In　response　to　this　need,　we　propose　a　color　temperature-based　lighting　source　classification　system　utilizing　autonomous　aerial　vehicle　(AAV)-captured　NTL　images,　rather　than　the　traditional　satellite-based　NTL　images,　due　to　the　superiority　of　spatial　resolution　(SR).　We　employ　an　object-oriented　classification　method　to　categorize　lights　into　high-pressure　sodium　(HPS),　warm　LEDs,　cool　LEDs,　and　colored　LEDs.　Moreover,　to　evaluate　the　effect　of　flight　altitude　on　classification　accuracy,　we　classify　lights　at　seven　different　altitudes　and　compare　their　accuracy　at　each　level.　Our　results　indicate　that　the　random　forest　(RF)　algorithm　can　accurately　identify　the　four　types　of　lights,　with　the　highest　classification　accuracy　achieved　at　a　flight　altitude　of　350　m,　where　the　overall　accuracy　(OA)　and　kappa　coefficient　were　0.957　and　0.947,　respectively.　Moreover,　at　this　altitude,　the　highest　producer＇s　accuracy　(PA)　for　warm　LEDs　and　colored　LEDs　was　0.971　and　0.942,　respectively,　while　the　user＇s　accuracy　(UA)　for　each　light　type　exceeded　0.9.　In　addition,　the　methodology　also　demonstrated　strong　performance　in　more　complicated　regions,　as　evidenced　by　an　off-site　application　accuracy　of　0.847　and　a　kappa　coefficient　of　0.808.　This　study　is　the　first　to　identify　NTL　types　based　on　color　temperature,　offering　a　new　perspective　for　urban　lighting　planning　and　light　pollution　management.