35 Years of Vegetation and Lake Dynamics in the Pechora Catchment, Russian European Arctic

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.

A Pixel-Based Vegetation Greenness Trend Analysis over the Russian Tundra with All Available Landsat Data from 1984 to 2018

As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies on the greenness trends in the Russian tundra have only been carried out at a limited local or regional scale and the spatial heterogeneity of the trend remains unclear. Here, we analyzed the fine spatial resolution dataset Landsat archive from 1984 to 2018 over the entire Russian tundra and produced pixel-by-pixel greenness trend maps with the support of Google Earth Engine (GEE). The entire Russian tundra was divided into six geographical regions based on World Wildlife Fund (WWF) ecoregions. A Theil–Sen regression (TSR) was used for the trend identification and the changed pixels with a significance level p < 0.05 were retained in the final results for a subsequent greening/browning trend analysis. Our results indicated that: (1) the number of valid Landsat observations was spatially varied. The Western and Eastern European Tundras (WET and EET) had denser observations than other regions, which enabled a trend analysis during the whole study period from 1984 to 2018; (2) the most significant greening occurred in the Yamal-Gydan tundra (WET), Bering tundra and Chukchi Peninsula tundra (CT) during 1984–2018. The EET had a greening trend of 2.3% and 6.6% and the WET of 3.4% and 18% during 1984–1999 and 2000–2018, respectively. The area of browning trend was relatively low when we first masked the surface water bodies out before the trend analysis; and (3) the Landsat-based greenness trend was broadly similar to the AVHRR-based trend over the entire region but AVHRR retrieved more browning areas due to spectral mixing adjacent effects. Higher resolution images and field measurement studies are strongly needed to understand the vegetation trend over the Russian tundra ecosystem.

46-Year (1973–2019) Permafrost Landscape Changes in the Hola Basin, Northeast China Using Machine Learning and Object-Oriented Classification

Land use and cover changes (LUCC) in permafrost regions have significant consequences on ecology, engineered systems, and the environment. Obtaining more details about LUCC is crucial for sustainable development, land conservation, and environment management. The Hola Basin (957 km2) in the northernmost part of Northeast China, a boreal forest landscape underlain by discontinuous, sporadic, and isolated permafrost, was selected for the case study. The LUCC was analyzed using the Landsat archive of satellite images from 1973 to 2019. A thematic change detection analysis was performed by combining the object-based image analysis (OBIA) and the Support Vector Machine (SVM) algorithm. Four types of LUCC (forest, grass, water, and anthropic) were extracted with an overall accuracy of 80% for 1973 and >90% for 1986, 2000, and 2019. Forest, the dominant class (750 km2 in 1973), declined by 88 km2 (11.8%) from 1973 to 1986 but had a recovery of 78 km2 (12.5%) from 2000 to 2019. Grass, the second-largest class (187 km2 in 1973), increased by 86 km2 (46.5%) between 1973 and 1986 and decreased by 90 km2 (40%) between 2000 and 2019. The anthropic class continuously increased from 10 km2 (1973) to 37 km2 (2019). Major features in LUCC are attributed to rapid population growth, resource exploitation, agriculture intensification, economic development, and frequent forest fires. Under a pronounced climate warming, these drivers have been accelerating the degradation of permafrost, subsequently triggering natural hazards and deteriorating the ecological environment. This study represents a benchmark for sustainable LUCC management in the Hola Basin, Northeast China.

Assessment of Spatio-Temporal Landscape Changes from VHR Images in Three Different Permafrost Areas in the Western Russian Arctic

Our study highlights the usefulness of very high resolution (VHR) images to detect various types of disturbances over permafrost areas using three example regions in different permafrost zones. The study focuses on detecting subtle changes in land cover classes, thermokarst water bodies, river dynamics, retrogressive thaw slumps (RTS) and infrastructure in the Yamal Peninsula, Urengoy and Pechora regions. Very high-resolution optical imagery (sub-meter) derived from WorldView, QuickBird and GeoEye in conjunction with declassified Corona images were involved in the analyses. The comparison of very high-resolution images acquired in 2003/2004 and 2016/2017 indicates a pronounced increase in the extent of tundra and a slight increase of land covered by water. The number of water bodies increased in all three regions, especially in discontinuous permafrost, where 14.86% of new lakes and ponds were initiated between 2003 and 2017. The analysis of the evolution of two river channels in Yamal and Urengoy indicates the dominance of erosion during the last two decades. An increase of both rivers’ lengths and a significant widening of the river channels were also observed. The number and total surface of RTS in the Yamal Peninsula strongly increased between 2004 and 2016. A mean annual headwall retreat rate of 1.86 m/year was calculated. Extensive networks of infrastructure occurred in the Yamal Peninsula in the last two decades, stimulating the initiation of new thermokarst features. The significant warming and seasonal variations of the hydrologic cycle, in particular, increased snow water equivalent acted in favor of deepening of the active layer; thus, an increasing number of thermokarst lake formations.