DDD Conference

Prof. Alexander Prishchepov

Multisource Imagery Reveals Different Proximate Drivers of Grassland Degradation and Recovery in the Dry Steppe Belt of Eurasia

University of Copenhagen, Denmark


The grasslands play a crucial role in global biogeochemical cycling, supporting economies and hosting biodiversity. Recent studies demonstrated partial recovery of steppes due to farmland abandonment due to agricultural decline, for instance, in Kazakhstan, Mongolia, Russia and Ukraine. But, presumably, the human footprint on steppes is far too complex and often missed from observations because a) research has been primarily concentrated on agricultural land-use changes, b) studies often relied on 30-m Landsat imagery, which may be insufficient to capture detailed land-cover change processes, such as informal roads, oil and gas development, shrub encroachment, military use, etc. Our major goal was to evaluate proximate drivers (immediate reasons) and patterns of grassland degradation and recovery in the dry steppe belt of European Russia and part of Eastern Ukraine and Kazakhstan from 1990 to 2018. We followed the UNCCD’s summary of factors, which may result in steppe degradation, and here concentrated on land-use change. We used in our study Landsat and Sentinel-1 and Sentinel-2 imagery and constructed seasonal cloud-free composites (Spring, Summer, Fall) circa 1990 and 2018 in Google Earth Engine (GEE). We used monthly Sentinel-2 composites to map mowing and tested SAR Sentinel-1 VV and VH polarization. By bringing the case study of Eastern Ukraine, Samara and Orenburg province of Russia, we revealed other disturbances in steppes, namely, oil and gas development, formal and informal roads, garbage dumps, and military land use (Eastern Ukraine). We tested a range of image classification techniques, such as the classification of land-cover change with a random forest classifier. We also tested U-Net Convolutional Neural Network architecture as well as crowdsource participatory approaches to map detailed disturbances in the steppes. Training and validation data have been collected with the use of very-high-resolution satellite imagery available via Google Earth, PlanetScope constellations, dense Landsat, Sentinel-2 composites. Work has also been complemented by validation data collection during the field campaigns in 2018-2019. Our study showed the recovery of steppe grasslands due to farmland abandonment from 1990 to 2018 particularly in Orenburg, Samara, Saratov, parts of Rostov provinces of Russia, and in insurgent parts Donetsk and Luhansk provinces in Eastern Ukraine, and in Kazakhstan. The results of broad-scale analysis correlated well with the change observed from the official agricultural statistics at the province level. The detailed zoom-in to Orenburg province in Russia also showed only 30% of grasslands were mowed in 2018. By looking at feature importance with random forest, we noticed SWIR bands and simple phenology metrics such as standard deviation calculated from May 1st to October 1st, contributed to improving the separability of thematic classes. Despite steppe recovery due to widespread cropland abandonment from 1990 to 2018, the steppes, including the recovered steppe patches, underwent fragmentation due to other disturbances. Systematic mapping with very high-resolution images revealed fragmentation of steppes due to informal roads, oil and gas development, shrub encroachment, and garbage dumps across Eastern Ukraine and Samara and Orenburg of Russia. Also, the remaining steppe patches both in controlled and insurgent parts of Eastern Ukraine additionally experienced land degradation due to military land use. The mapped disturbances occurred primarily near settlements and roads, except for military use in Ukraine. Only 7% of analyzed areas for detailed disturbances were without such disturbances and were often found in age-environmentally marginal areas, such as the south and southeast corner of Orenburg province of Russia. Our experiments with U-Net CNN showed the great potential of this classification approach. Our study showed a great advantage of using Sentinel-1, Sentinel-2, Landsat time series to map the recovery of steppes. A multisensory approach, such as applying very-high-resolution imagery, is a way of mapping the detailed disturbances that are difficult to capture with Sentinel-1, Sentinel-2, Landsat time series. The presented study can be relevant beyond the Eurasian steppes, and the approach can be adapted to map disturbances of grasslands and other biomes and their degradation in other parts of the world.

Skip to content