Some sloping peatlands in northern regions often develop surface microtopographic patterns to maintain their water balance and ecosystem functioning. However, we do not know whether and how spatial patterning would influence the water balance and peat formation of permafrost-affected peatlands in relatively dry regions. Here we used data from the field observations and Unmanned Aerial Vehicle (UAV) survey of a slope peatland at an elevation of around 4800 m in the hinterland of the Qinghai-Tibetan Plateau (QTP) to document and understand the topographic controls of water balance and vegetation growth. Our terrain analysis result shows that the peatland-located on the middle of a hillslope-has a gentle slope of 5.6 degrees +/- 2.5 degrees, while the non-peatland upper has a steep slope of 12 degrees +/- 4.5 degrees. The great upstream catchment area and the presence of shallow impermeable permafrost likely create a saturated condition for peat formation. Our UAV results show obvious spatial patterning of abundant pools and ridges across this peatland, and pool sizes and ridge abundance increase with increasing slopes, suggesting that slope-controlled water flow gradient is the main driver of ridge formation and that ridges is to slow down the runoff. UAV-derived greenness values show a positive relationship with the total pool extent locally (R2 = 0.60) and decrease with increasing distance from the individual pools, suggesting sensitive responses of vegetation growth to surface moisture. Thus, enhanced vegetation growth and likely resultant great peat accumulation immediately around pools potentially further differentiate surface micro-topography, strengthening the pool stability. We conclude that the local slope gradient, surface patterning (pools and ridges) and permafrost interact together to regulate water flow and maintain water balance, which in turn regulate the vegetation growth, peat accumulation and peatland stability. Our study implies that the delicate water balance maintained partly by microtopography is sensitive to climate change-especially potential extreme hydroclimate events-and natural and human-induced disturbances that may modify the surface patterning and weaken the peatland's stability, affecting the carbon sequestration ability of this type of peatlands.

The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant changes due to climate change. There is still a lack of high -precision simulation methods for alpine grassland cover (AGC), and the climate feedback mechanisms of AGC remain unclear, which poses challenges for the production of highprecision AGC products and the formulation of ecological conservation policies. In this study, a transferable stacking deep learning (Stacking -DL) model is proposed based on a CNN, a DNN, and a GRU for AGC time series simulation. The applicability of deep learning models for AGC simulation is evaluated based on long time series of measured data, MODIS data, and environmental factors. Finally, the AGC spatiotemporal changes and controlling environmental factors in the alpine region were analyzed based on Sen 's slope and structural equation modeling (SEM). The results showed that feature selection and parameter optimization improved the applicability of the deep learning models in AGC simulations, and the DNN (R 2 = 0.899, RMSE = 0.078) model performed best among the base deep learning models. The Stacking -DL model combines the advantages of multiple models and achieves high transfer accuracy. In the YRSR, the AGC increase area (20.34 %) is greater than the AGC decrease area (3.34 %), the increase area is mainly located in the northeast, and the decrease area is mainly located in the southwest. AGC changes in the YRSR are mainly controlled by permafrost and climate. This study provides a high -precision and transferable vegetation monitoring model for alpine mountain regions based on advanced deep learning models and clarifies the response mechanism of AGC under climate change.

Plateau pika (Ochotona curzoniae, hereafter pika) is considered to exert a profound impact on vegetation species diversity of alpine grasslands. Great efforts have been made at mound or quadrat scales; nevertheless, there is still controversy about the effect of pika. It is vital to monitor vegetation species composition in natural heterogeneous ecosystems at a large scale to accurately evaluate the real role of pika. In this study, we performed field survey at 55 alpine grassland sites across the Shule River Basin using combined methods of aerial photographing using an unmanned aerial vehicle (UAV) and traditional ground measurement. Based on our UAV operation system, Fragmentation Monitoring and Analysis with aerial Photography (FragMAP), aerial images were acquired. Plot-scale vegetation species were visually identified, and total pika burrow exits were automatically retrieved using the self-developed image processing software. We found that there were significant linear relationships between the vegetation species diversity indexes obtained by these two methods. Additionally, the total number of identified species by the UAV method was 71, which was higher than the Quadrat method recognition, with the quantity of 63. Our results indicate that the UAV was suitable for long-term repeated monitoring vegetation species composition of multiple alpine grasslands at plot scale. With the merits of UAV, it confirmed that pika's disturbance belonged to the medium level, with the density ranging from 30.17 to 65.53 ha(-1). Under this density level, pika had a positive effect on vegetation species diversity, particularly for the species richness of sedge and forb. These findings conclude that the UAV was an efficient and economic tool for species monitoring to reveal the role of pika in the alpine grasslands.

Seasonal subsidence induced by ground ice melt can be measured by interferometric synthetic aperture radar (InSAR) techniques to infer active layer thickness (ALT) in permafrost regions. The magnitude of subsidence depends on both how deep the soil thawed and how much ice/water content existed in the active layer soil. To provide the later, P-band polarimetric synthetic aperture radar (PolSAR) backscatter is used due to its sensitivity to subsurface soil moisture and freeze/thaw conditions. In this study, which is the second in a two-part series of Permafrost Dynamics Observatory (PDO), we exploit L-band InSAR subsidence and P-band PolSAR backscatter in a joint retrieval scheme to simultaneously estimate ALT and soil moisture profile of permafrost active layer. Both subsidence and backscatter are explicitly characterized by physics-based models and share a common set of soil parameters including porosity and water saturation profiles. The PDO joint retrieval has been applied to the L- and P-band SAR data acquired by National Aeronautics and Space Administration/Jet Propulsion Laboratory 's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and western Canada during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign. This high-resolution (30 m) regional estimates of ALT and soil moisture profile spanning over the ABoVE study domain can help link the ground-based field surveys with satellite observations to further understand the permafrost and active layer soil process dynamics to disturbances and climate change occurring across the northern circumpolar region.

In recent years, researchers have focused on the applications of uncrewed aerial vehicles (UAVs) in environmental remote sensing tasks. However, studies on glacier monitoring using UAV technology are relatively scarce, especially for high mountain glacier monitoring. To explore the feasibility of UAV technology for high mountain glaciers, four UAV surveys were deployed on two glaciers of the central Tibetan Plateau. Based on the images retrieved by UAV in 2017 and 2019, orthomosaics and digital elevation models were produced to quantify the length, area and elevation changes in the ablation zone of these two glaciers at different times. Additionally, we utilized several Landsat scenes to derive glacier changes over the last 30 years and combined these with the UAV data to assess the advantages and disadvantages of UAV technology in mountain glacier monitoring.

Mapping accurately vegetation surfaces in space and time in the ice-free areas of Antarctica can provide important information to quantitatively describe the evolution of their ecosystems. Spaceborne remote sensing is the adequate way to map and evaluate multitemporal changes on the Antarctic vegetation at large but its nature of occurrence, in relatively small and sparse patches, makes the identification very challenging. The inclusion of an intermediate scale of observation between ground and satellite scales, provided by Unmanned Aerial Vehicles (UAV) imagery, is of great help not only for their effective classification, but also for discriminating their main communities (lichens and mosses). Thus, this paper quantifies accurately recent changes of the vegetated areas in Fildes Peninsula (King George Island, Antarctica) through a novel methodology based on the integration of multiplatform data (satellite and UAV). It consists of multiscale imagery (spatial resolution of 2 m and 2 cm) from the same period to create a robust classifier that, after intensive calibration, is adequately used in other dates, where field reference data is scarce or not available at all. The methodology is developed and tested with UAV and satellite data from 2017 showing overall accuracies of 96% and kappa equal to 0.94 with a SVM classifier. These high performances allow the extrapolation to a pair of previous dates, 2006 and 2013, when atmospherically clear very high-resolution satellite imagery are available. The classification allows verifying a loss of the total area of vegetation of 4.5% during the 11-year time period under analysis, which corresponds to a 10.3% reduction for Usnea sp. and 9.8% for moss formations. Nevertheless, the breakdown analysis by time period shows a distinct behaviour for each vegetation type which are evaluated and discussed, namely for Usnea sp. whose decline is likely to be related to changing snow conditions. (C) 2019 Elsevier B.V. All rights reserved.

In this paper, we evaluate the impact of mineral dust (MD) on snow radiative properties in the European Alps at ground, aerial, and satellite scale. A field survey was conducted to acquire snow spectral reflectance measurements with an Analytical Spectral Device (ASD) Field Spec Pro spectroradiometer. Surface snow samples were analyzed to determine the concentration and size distribution of MD in each sample. An overflight of a four-rotor Unmanned Aerial Vehicle (UAV) equipped with an RGB digital camera sensor was carried out during the field operations. Finally, Landsat 8 Operational Land Imager (OLI) data covering the central European Alps were analyzed. Observed reflectance evidenced that MD strongly reduced the spectral reflectance of snow, in particular, from 350 to 600nm. Reflectance was compared with that simulated by parameterizing the Snow, Ice, and Aerosol Radiation radiative transfer model. We defined a novel spectral index, the Snow Darkening Index (SDI), that combines different wavelengths showing nonlinear correlation with measured MD concentrations (R-2=0.87, root-mean-square error=0.037). We also estimated a positive instantaneous radiative forcing that reaches values up to 153W/m(2) for the most concentrated sampling area. SDI maps at local scale were produced using the UAV data, while regional SDI maps were generated with OLI data. These maps show the spatial distribution of MD in snow after a natural deposition from the Saharan desert. Such postdepositional experimental data are fundamental for validating radiative transfer models and global climate models that simulate the impact of MD on snow radiative properties.