Driving factors of formation of top-down gravelization encroachment in alpine hillslopes grasslands
Description
Long-term Landsat series satellite images were used as data to monitor the dynamic changes of sloped grassland in all study sites. Landsat images with a 16-day revisit comprise seven multi-spectral bands with spatial resolution of 30 m (Senf et al., 2020). We hand-selected annual Landsat images closest to the phenological peak (defined as the mid-July to mid- August) and with little to no cloud cover in the study region (Piao et al., 2012). We screened United States Geological Survey (https://earthexplorer.usgs.gov/) archive to maximize time coverage, obtaining 9 to 20 images in four study areas from 1985 to 2020 (Table 2). The earliest image was 3rd of June and the latest image was 29th of September. The Digital Elevation Model (DEM) with spatial resolution of 12.5 m was downloaded from the United States Geological Survey, and the slope data derived from it. 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 (http://www.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.
Files
Steps to reproduce
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 a valuable way for validation of unmixing model. To acquire images from GE, we bought the copyright of Bigemap (http://www.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 (Figure 2). 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 in each study area, and then by comparing coverage in Landsat and GE images. For validation, R2 of each image above 0.8 was considered to ensure the accuracy of the unmixing model results. For each GE image, vegetation coverage (e) was calculated from the RGB values in which green vegetation was enhanced using the following formula e=2g-r-b (1) where g, r, and b are the digital values in the green, red, and blue channels. By applying a manual threshold, we selected grassland pixels and calculated the grassland coverage of the green vegetation in the selected images for the entire period (1985-2020). Surface air temperature has been a widely recognized climate factor used to evaluate the effects of climate warming, among other things. Yet, recent studies highlight that the thermodynamic parameter known as surface equivalent potential temperature (SEPT) could be a more appropriate metric of climate warming since it integrates both temperature and humidity changes (Song et al., 2022). Here, we use SEPT to evaluate the climate evolution in the study regions over the analyzed time period. To compute SEPT (Eq. 2, Song et al., 2022), 2 m air temperature, surface specific humidity, and surface pressure were downloaded from European Centre for Medium-Range Weather Forecasts reanalysis (ERA5).
Categories
Funders
- National Natural Science Foundation of ChinaGrant ID: NSFC 41722107
- the Strategic Priority Research Program of the Chinese Academy of SciencesGrant ID: XDB40000000
- Natural Science Foundation of Qinghai ProvinceGrant ID: 2020-ZJ-726