Driving factors of formation of top-down gravelization encroachment in alpine hillslopes grasslands

Grassland cover fraction mapping and validation The regression-based spectral unmixing approach has been demonstrated to be a powerful technique for extracting surface materials at a sub-pixel scale (Pacheco et al., 2010; Senf et al., 2020). To apply this technique, the images were used to create a spectral library with pure cover type spectra representative of the study area. Pure pixels were defined as unchanged pixel representing a type of cover (grassland, gravel, glacier or water) during all the years, and surrounded by pure pixels to avoid errors from spatial mis-registration.

Overall, we collected 300 pure spectra as training data in each study area and surrounding area. The training data sets were used to train a fully constrained least squares (FCLS) unmixing model predicting the coverage from the mixed Landsat spectra. The trained model was finally applied to each Landsat image, continuously predicting grassland cover fractions throughout all 30 years.

High spatial resolution remote sensing images in Google Earth (GE) provides available way for validation of unmixing model. To acquire images from GE, we bought the copyright of Bigemap (bigemap.com/), which is a downloader of satellite image in GE. Eight remote sensing images (0.25-0.5 m) were downloaded for validation of unmixing results.

Table 3 lists the locations, acquisition dates, and spatial resolutions of those GE images. We validated the model predictions by randomly sampling 50 reference Landsat pixels across years of GE images correspond in each study area, by comparing coverage in Landsat and GE images.