Continuous monitoring of forest disturbance using all available Landsat imagery

A new change detection algorithm for continuous monitoring of forest disturbance at high temporal frequency is developed. Using all available Landsat 7 images in two years, time series models consisting of sines and cosines are estimated for each pixel for each spectral band. Dropping the coefficients that capture inter-annual change, time series models can predict surface reflectance for pixels at any location and any date assuming persistence of land cover. The Continuous Monitoring of Forest Disturbance Algorithm (CMFDA) flags forest disturbance by differencing the predicted and observed Landsat images. Two algorithms (single-date and multi-date differencing) were tested for detecting forest disturbance at a Savannah River site. The map derived from the multi-date differencing algorithm was chosen as the final CMFDA result, due to its higher spatial and temporal accuracies. It determines a disturbance pixel by the number of times “change” is observed consecutively. Pixels showing “change” for one or two times are flagged as “probable change”. If the pixel is flagged for the third time, the pixel is determined to have changed. The accuracy assessment shows that CMFDA results were accurate for detecting forest disturbance, with both producer's and user's accuracies higher than 95% in the spatial domain and temporal accuracy of approximately 94%.

► A new continuous monitoring of forest disturbance algorithm (CMFDA).
► Pixels showing “change” for one or two times are flagged as “probable change”.
► Pixels showing “change” for the third times are determined to have changed.
► Both producer's and user's accuracies of CMFDA are higher than 95%.
► Temporal accuracy of CMFDA is approximately 94%.