Impacts of imagery temporal frequency on land-cover change detection monitoring

An important consideration for monitoring land-cover (LC) change is the nominal temporal frequency of remote sensor data acquisitions required to adequately characterize change events. Ecosystem-specific regeneration rates are an important consideration for determining the required frequency of data collections to minimize change omission errors. Clear-cut forested areas in north central North Carolina undergo rapid colonization from pioneer (replacement) vegetation that is often difficult to differentiate spectrally from that previously present. This study compared change detection results for temporal frequencies corresponding to 3-, 7-, and 10-year time intervals for near-anniversary date Landsat 5 Thematic Mapper (TM) data acquisitions corresponding to a single path/row. Change detection was performed using an identical change vector analysis (CVA) technique for all imagery dates. Although the accuracy of the 3-year analysis was acceptable (86.3%, κ=0.55), a significant level of change omission errors resulted (51.7%). Accuracies associated with both the 7-year (43.6%, κ=0.10) and 10-year (37.2%, κ=0.05) temporal frequency analyses performed poorly, with excessive change omission errors of 84.8% and 86.3%, respectively. The average rate of LC change observed over the study area for the 13-year index period (1987–2000) was approximately 1.0% per annum. Overall results indicated that a minimum of 3–4-year temporal data acquisition frequency is required to monitor LC change events in north central North Carolina. Reductions in change omission errors could probably best be achieved by further increasing temporal data acquisition frequencies to a 1–2-year time interval.