Error Sources in Deforestation Detection Using BFAST Monitor on Landsat Time Series Across Three Tropical Sites

Accurate tropic deforestation monitoring using time series requires methods which can capture gradual to abrupt changes and can account for site-specific properties of the environment and the available data. The generic time series algorithm BFAST Monitor was tested using Landsat time series at three tropical sites. We evaluated the importance of how specific effects of site and radiometric correction affected the accuracy of deforestation monitoring when using BFAST Monitor. Twelve sets of time series of normalized difference vegetation index (NDVI) Landsat data (2000–2013) were analyzed. Time series properties varied according to site (Brazil, Ethiopia, and Vietnam) and which correction scheme was applied: Atmospheric Correction and Haze Reduction 2 and 3 (ATCOR 2 and 3), Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), or Dark Object Subtraction (DOS). Mapping accuracy was compared using 1200 reference points per site and consistent designs for sampling, analysis (overall accuracy, user’s accuracy, and producer’s accuracy), and response (ground truth and very-high-resolution data). With the exception of DOS, mapping accuracies across correction methods were found to be similar but varied greatly with site. Mapping errors were modeled using a set of error parameters that yielded information on data and site-specific environmental properties. Important parameters for characterizing mapping errors were found to be variance of the NDVI and soil signal as well as availability of time series data, and forest edge effects. Based upon the results, local fine-tuning of the algorithm is essential for some areas but for others default settings create satisfactory accuracies.Accurate tropic deforestation monitoring using time series requires methods which can capture gradual to abrupt changes and can account for site-specific properties of the environment and the available data. The generic time series algorithm BFAST Monitor was tested using Landsat time series at three tropical sites. We evaluated the importance of how specific effects of site and radiometric correction affected the accuracy of deforestation monitoring when using BFAST Monitor. Twelve sets of time series of normalized difference vegetation index (NDVI) Landsat data (2000–2013) were analyzed. Time series properties varied according to site (Brazil, Ethiopia, and Vietnam) and which correction scheme was applied: Atmospheric Correction and Haze Reduction 2 and 3 (ATCOR 2 and 3), Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), or Dark Object Subtraction (DOS). Mapping accuracy was compared using 1200 reference points per site and consistent designs for sampling, analysis (overall accuracy, user’s accuracy, and producer’s accuracy), and response (ground truth and very-high-resolution data). With the exception of DOS, mapping accuracies across correction methods were found to be similar but varied greatly with site. Mapping errors were modeled using a set of error parameters that yielded information on data and site-specific environmental properties. Important parameters for characterizing mapping errors were found to be variance of the NDVI and soil signal as well as availability of time series data, and forest edge effects. Based upon the results, local fine-tuning of the algorithm is essential for some areas but for others default settings create satisfactory accuracies