Datasets used

We used all available Landsat TM, ETM+, and OLI imagery from the snowless season of each year from 1985 to 2017. We used Tier-1 surface reflectance imagery, atmospherically corrected with LEDAPS (for TM and ETM+) and LASRC (for OLI) as available from USGS via Google Earth Engine. Clouds, cloud shadows and snow were masked using CFMASK (Foga et al., 2017).

To assess the accuracy of the remote sensing analyses, we used data from 843 vegetation plots of 10x10 m each (Table IV-1). We collected these vegetation data across the study area along a gradient of grazing pressure and across all major ecozones (productive steppe,

independently from each other, meaning each point was visited once. We used a systematic sampling design to account for a possible sampling bias (Congalton and Green, 2008).

Field observations from every year and a random sampling design could have increased reliability of our area estimates, however, the latter was not possible due to logistical reasons, and the ground data from early years of our study period do not exist to our best knowledge. At each plot, we recorded the cover of all plant species, total vegetation cover, bare ground, and vegetation height. We allocated the vegetation plots to three classes of grazing pressure (‘heavily grazed’, ‘moderately grazed’ and ‘ungrazed’) for the analysis.

The classification was based on a visual, in situ assessment, and the final decision was made based on the plant species composition, cover of bare ground, live and dead biomass and density of dung piles. Plots were classified as moderately grazed if clear signs of grazing were visible, such as partially defoliated plants, few dead biomass and dung piles, while a high cover of bare ground and characteristic plant species (e.g. annuals, certain Artemisia species) indicated heavy grazing (Figure IV-2). We also collected biomass by cutting all herbaceous biomass at five quadrats of 0.1 m² randomly placed within 335 independently sampled plots. We weighed biomass after drying for 48h at 70°C (for further details of vegetation, biomass and dung pile sampling see Brinkert et al. (2016). In addition, at these 335 plots, we counted the dung piles of sheep, cattle and horses along a strip transect of 100m length and 2m width. This transect was centered on the plot (i.e. we always walked 50m away from the plot in eastern and western direction). The dung estimate hence characterizes the grazing intensity in the immediate vicinity of the plot, considering that plots were spaced at least 2 km apart from each other (Laing et al., 2003).

Table IV-1: Summary of field data collected during field campaigns in the period 2009 to 2016, including the sampling methods, number of samples, and specific time period covered by our different field datasets.

Type Sampling method Time period # of plots

Levels of grazing

pressure Expert-based classification in the fields in 10x10 m² vegetation plots. Three levels of grazing (high, moderate, low) were distinguished based on a visual assessment of signs of grazing, dung density, species composition, total vegetation cover, bare ground, and vegetation height.

2009, 2010, 2015, 2016 843

Biomass estimation All herbaceous biomass was harvested in plots consisting

of 5 quadrats of 0.1 m² each, dried, weighted and averaged. 2015, 2016 335 Dung piles counting Dung piles of sheep, cattle and horses were counted on

transects of 100m x 2m, centered on a 10x10 m² vegetation plot. Dung pile values were averaged.

2015, 2016 335

As additional indicators for grazing pressure, we digitized winter and summer livestock stations (‘zimovkas’ and ‘letovkas’ in Russian) and settlements (hereafter jointly referred to

late 1980s (VTU GSh, 1989). Zimovkas and letovkas, across the former Soviet Union, are outposts on summer or winter pastures, where livestock is concentrated that does not need to be kept in sheds overnight. The stations usually consist of one to three houses or tents (‘yurts’) for shepherd accommodation and a corral for livestock shelter during the night.

We checked if these livestock concentration points were still actively used based on recent high-resolution imagery from Google Earth (mostly from 2010 to 2014, with peak availability in 2013). We assigned a usage intensity index from 0 to 100, based on the share of intact infrastructure. This can be identified reliably from satellite imagery as active corrals, where livestock is kept overnight, accumulate dung and have animal tracks leading to them, while abandoned infrastructure collapses quickly in the area as they are predominantly built from clay bricks.

Figure IV-2: Examples of vegetation plots showing heavily grazed (A), moderately grazed (B), and ungrazed (C) steppes from the field campaign in June 2015. The bottom row shows the respective time series of Tasseled Cap brightness (TCB), greenness (TCG), and wetness (TCW) values in the snowless period of that year. Grazed plots demonstrate generally higher TCG values than the ungrazed plots. Note also that grazed plots, and particularly heavily grazed plots have an earlier greenup and a distinct second vegetation peak early in the season.

As ancillary data, we used a Landsat-based land-cover/change map (Baumann et al., In review), and a soil map (Beznosov and Uspanov, 1960). We also used official rayon (=

district)-level time series on the number of cattle, horses, sheep, and goats from the Statistics Agency of the Republic of Kazakhstan (KazStat, 2019) from 1990 to 2017, and aggregated them to livestock units according to EUROSTAT conversion factors (horses = 1, cattle = 0.8, sheep and goats = 0.1 units, Eurostat, 2013). Comparing livestock numbers to those available at FAOSTAT for the whole country suggests our study area is representative for the entire Kazakh steppe. We used an aridity index based on the WorldClim2 Global Climate Data (Fick and Hijmans, 2017; Trabucco and Zomer, 2018).

Finally, we acquired monthly rayon-level precipitation and temperature data from the Climatic Research Unit (CRU, Harris et al., 2014) and aggregated them into annual mean temperature and total precipitation time series for 1985-2017.