Image　Change　Detection　(ICD)　methods　are　widely　adopted　to　update　large　area　land　use/cover　products.　Uncertainty　problems,　however,　are　well　known　in　such　techniques,　and　a　transparent　assessment　is　necessary.　In　this　study,　a　framework　was　proposed　for　evaluating　binary　land　change　utilizing　remote　sensing　images.　First,　two　widely　adopted　ICD　methods　were　used　to　establish　change　maps.　Second,　binary　decisions　on　Change　(C)　and　Non-Change　(NC)　classes　were　reached　through　thresholding　on　change　maps.　Then,　results　were　evaluated　using　two　sampling　designs:　random　sampling　and　stratified　sampling.　Analysis　of　results　suggests　that　(1)　for　random　sampling,　with　an　increasing　threshold　on　change　variables,　the　overall　accuracy　increases　and　shows　a　large　variance,　which　is　highly　correlated　with　the　C　omission　error;　and　(2)　comparatively,　for　stratified　sampling,　in　which　two　strata　(i.e.,　C　and　NC)　were　set,　the　overall　accuracy　shows　a　smaller　variance　and　is　highly　associated　with　the　NC　commission　error.　The　significant　trends　in　accuracy　assessments　indicate　the　trade-offs　between　the　C　and　NC　classification　errors　in　a　binary　decision　and　can　present　superficial　or　perfunctory　accuracy　evaluation　in　certain　circumstances　that　the　causes　of　error　sources　and　uncertainty　problems　in　ICD　are　not　fully　understood.