Monitoring forest changes based on numerous satellite images has been recently conducted in the tropics. Preparation of a time series of satellite images, sometimes referred to as preprocessing, is essential for conducting robust detection of forest change. To create consistent and stable conditions in satellite images, the best methods have to be used in each step to correct various sources of noise. This study assessed three atmospheric correction methods, six topographic correction methods, and eight gap-filling methods to produce the best possible time series of Landsat images of tropical seasonal forests. The results showed that the best methods for atmospheric and topographic correction were relative corrections using a Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), which is based on a 6S radiative transfer model, and the C-correction, respectively. Weighted linear regression and multiple linear regression models were selected as the best models for the filling of data gaps associated with Scan Line Corrector-off and clouds, respectively. This study provided the best possible image preprocessing for trajectory-based change detection using annual Landsat images. Although the best possible preprocessing methods might vary depending on the change detection methods used in different study areas, the results highlight the preferable preprocessing methods, even for different types of time series analysis.
All Science Journal Classification (ASJC) codes