One　of　the　major　barriers　to　hindering　the　sustainable　development　of　the　terrestrial　environment　is　the　desertification　process,　and　revegetation　is　one　of　the　most　significant　duties　in　anti-desertification.　Desertification　deteriorates　land　ecosystems　through　species　decline,　and　remote　sensing　is　becoming　the　most　effective　way　to　monitor　desertification.　Mu　Us　Sandy　Land　is　the　fifth　largest　desert　and　the　representative　area　under　manmade　vegetation　restorations　in　China.　Therefore,　it　is　essential　to　understand　the　spatiotemporal　characteristics　of　artificial　desert　transformation　for　seeking　the　optimal　revegetation　location　for　future　restoration　planning.　However,　there　are　no　previous　studies　focusing　on　exploring　regular　patterns　between　the　spatial　distribution　of　vegetation　restoration　and　human-related　geographical　features.　In　this　study,　we　use　Landsat　satellite　data　from　1986　to　2020　to　achieve　annual　monitoring　of　vegetation　change　by　a　threshold　segmentation　method,　and　then　use　spatiotemporal　analysis　with　Open　Street　Map　(OSM)　data　to　explore　the　spatiotemporal　distribution　pattern　between　vegetation　occurrence　and　human-related　features.　We　construct　an　artificial　vegetation　restoration　suitability　index　(AVRSI)　by　considering　human-related　features　and　topographical　factors,　and　we　assess　artificial　suitability　for　vegetation　restoration　by　mapping　methods　based　on　that　index　and　the　vegetation　distribution　pattern.　The　AVRSI　can　be　commonly　used　for　evaluating　restoration　suitability　in　Sandy　areas　and　it　is　tested　acceptable　in　Mu　Us　Sandy　Land.　Our　results　show　during　this　period,　the　segmentation　threshold　and　vegetation　area　of　Mu　Us　Sandy　Land　increased　at　rates　of　0.005/year　and　264.11　km2/year,　respectively.　Typically,　we　found　the　artificial　restoration　vegetation　suitability　in　Mu　Us　area　spatially　declines　from　southeast　to　northwest,　but　eventually　increases　in　the　most　northwest　region.　This　study　reveals　the　revegetation　process　in　Mu　Us　Sandy　Land　by　figuring　out　its　spatiotemporal　vegetation　change　with　human-related　features　and　maps　the　artificial　revegetation　suitability.　©　2022　by　the　authors.