Forest is one of the most important surface coverage types. Monitoring its dynamics is of great significance in global ecological environment monitoring and global carbon circulation research. Forest monitoring based on Landsat time-series stacks is a research hotspot, and continuous change detection is a novel approach to real-time change detection. Here, we present an approach, continuous change detection and classification-spectral trajectory breakpoint recognition, running on Google Earth Engine (GEE) for monitoring forest disturbance and forest long-term trends. We used this approach to monitor forest disturbance and the change in forest cover rate from 1987 to 2020 in Nanning City, China. The high-resolution Google Earth images are collected for the validation of forest disturbance. The classification accuracy of forest, non-forest, and water maps by using the optima classification features was 95.16%. For disturbance detection, the accuracy of our map was 86.4%, significantly higher than 60% of the global forest change product. Our approach can successfully generate high-accuracy classification maps at any time and detect the forest disturbance time on a monthly scale, accurately capturing the thinning cycle of plantations, which earlier studies failed to estimate. All the research work is integrated into GEE to promote the use of the approach on a global scale.

It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions.

Landsat time series, as the longest fine resolution dataset, has the most significant limitation of relatively low temporal frequency (16 days). However, the presence of clouds, cloud shadows, snow, and the failure of the sensor further reduces the amount of clear data, resulting in insufficient observations, which can easily lead to biases in statistical metrics (i.e., maximum, mean, percentiles, etc.). In this study, we took advantage of Google Earth Engine and proposed a Statistical Time-series biAs Modification Model (STAMM) that can generate real Landsat statistical metrics (i.e., Landsat statistical metrics based on sufficient observations) in large regions. STAMM can also quantitatively evaluate the bias of statistical metrics due to insufficient observations. Results show that the original Landsat NDVI 75 ^th percentiles over both the northeast and northwest of China overestimated about 10%~30% compared with real Landsat percentiles; while in the southwest of China, it underestimated about 10%. This issue has persisted for the past 20 years. As for other percentiles, the amount of bias depends on the data distribution. The bias of the 95 ^th percentile is relatively small when there are more clear observations in the high-value period. However, at this time, the bias of the 50 ^th percentile bias is relatively large. Taking the Sentinel percentiles as references, STAMM effectively improves the accuracy of Landsat percentiles (i.e., the RMSE decreased from 0.065 to 0.045). It is supposed to be able to provide consistent and accurate Landsat percentiles in large-region and long-time-series for better studying the inter-annual change of Earth’s surface.