Evaluation of annual forest disturbance monitoring using a static decision tree approach and 250 m MODIS data

摘要

Research on change detected has largely focused on method development and evaluation in a temporally dependent manner where training and validation data are from the same temporal period. Monitoring over several change periods needs to account for increased variability resulting from possible combinations of atmosphere, sensor, and surface conditions. Training a change method for each monitoring period (i.e. a dynamic approach) is an option, but can be costly to develop the needed training datasets and may not be warranted if sufficient accuracy can be obtained without retraining (i.e. a static approach). In this research the potential of change detection using a static approach suitable for near-real time annual monitoring was evaluated. The research assessed the influence of feature set size, radiometric normalization, incorporation of temporal information, and change object size and sub-pixel fraction on accuracy. The static approach was based on a decision tree developed using 250 m MODIS data from 2005 to 2006 and applied annually for the period 2001–2005. Change results between years were combined and compared to reference data representing change from 2001 to 2005 to evaluate monitoring performance. Results revealed high accuracy for the decision tree change model development from 2005 to 2006 (bootstrap cross-validation KAPPA=0.91), with lower accuracy (KAPPA=0.80) when applied for monitoring from 2001 to 2005.Radiometric normalization increased monitoring accuracy (KAPPA=0.86). Further improvement was achieved with the incorporation of temporal contextual tests to combine the 2001–2005 inter-annual change maps (KAPPA=0.90), but required a time lag of 1 year. An alternative temporal test that was not restricted by the 1 year time lag produced slightly lower accuracy (KAPPA=0.88). Evaluation of the effect of object size on detection accuracy showed that accuracy for objects less than 7 pixels was strongly related to object size, with objects less than 3 pixels having low detection rates. The effect of sub-pixel change fraction was found to be dependent on object size with larger objects reducing detection error across the range of fractions evaluated.