Abstract
The increasing availability of dense time series of earth observation data has incited a growing interest in time series analysis for vegetation monitoring and change detection. Vegetation monitoring algorithms need to deal with several time series characteristics such as seasonality, irregular sampling intervals, and signal artefacts. While common algorithms based on deterministic harmonic regression models account for intra-annual seasonality, inter-annual variations of the seasonal pattern related to shifts in vegetation phenology due to different temperature and rainfall are usually not accounted for. We propose a transition to stochastic modelling and present a near real-time change detection method that combines a structural time series model with the Kalman filter. The model continuously adapts to new observations and allows to better separate phenology-related deviations from vegetation anomalies or land cover changes. The method is tested in a forest change detection application aiming at the assessment of damages caused by storm events and insect calamities. Forest changes are detected based on the cumulative sum control chart (CUSUM) which is used to decide if new observations deviate from model-based forecasts. The performance is evaluated in two test sites, one in Malawi (dry tropical forest) and one in Austria (temperate deciduous, coniferous and mixed forests) based on Sentinel-2 time series. Both forest areas are characterized by a distinct, but temporally varying leaf-off season. The presented change detection method shows overall accuracies above 99%, users’ accuracies of 76.8% to 88.6%, and producers’ accuracies of 68.2% to 80.4% for the forest change stratum (minimum mapping unit: 0.1 ha). Results are based on visually interpreted points derived by stratified random sampling. A further analysis revealed that increasing the time series density by merging data from two Sentinel-2 orbits yields better forest change detection accuracies in comparison to using data from one orbit only. The resulting increase in users’ accuracy amounts to 7.6%. The presented method is capable of near real-time processing and could be used for a variety of automated forest monitoring applications.
Highlights
Current Earth observation (EO) missions employing optical sensors such as Sentinel-2 acquire a vast volume of data: a new image every 5 days of almost every place on earth
The detailed results of the forest disturbance detection are shown in Tables 3–7, where the upper part presents the sample counts and the lower part presents the unbiased estimates of area proportion and accuracy measures
While our findings suggest that denser time series resulting from orbit overlaps lead to higher forest change mapping accuracies, it is not yet clear if these findings for the dry forests in Malawi are valid for other forest types and for other regions
Summary
Current Earth observation (EO) missions employing optical sensors such as Sentinel-2 acquire a vast volume of data: a new image every 5 days of almost every place on earth. By taking orbit overlaps into account, the time between consecutive images of the same region is reduced even further and the chance of acquiring cloud-free observations is further increased. Through high-quality georeferencing and atmospheric correction of the satellite images, it is possible to create consistent time series of measured reflectance values for any given spectral band. The vast availability of high-resolution optical data allows—for the first time—to map small changes in near real-time. Dense time series of high-resolution optical data have a number of characteristics that pose a challenge to change detection applications. In addition to noise effects remaining after atmospheric correction and uncertainties in the geometric registration, these challenging characteristics include:
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