Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

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.

Global warming has accelerated during the past decades, causing a dramatic shrinking of glaciers across the globe. So far, the attempts to counterbalance glacial melt have proven to be inadequate and are mostly limited to a few glacial landscapes only. In the present study, a scientific glacier protection experiment was conducted at the Dagu Glacier site. Specifically, the study site was the Dagu Glacier No. 17, situated 4830 m a.s.l. The study involved a deliberate verification of the feasibility and effectiveness of using geotextile covers on small glaciers located at high altitudes between August 2020 and October 2021. The observations revealed that the mass loss in the area covered with geotextiles was, on average, 15% lower (per year) compared to that in the uncovered areas combining field campaigns, terrestrial laser scanning, and unmanned aerial vehicle. The reason for this could be that the albedo of the geotextile is higher than that of the glacier surface. In addition, the aging of geotextiles causes a decline in their albedo, leading to a gradual decline in the effectiveness of the resulting glacier protection. It was indicated that geotextiles could be effective in facilitating the mitigation of glacier ablation, although the cost-related limitations render it difficult to upscale the use of artificial cover. Nonetheless, using active artificial cover could be effective in the case of small glaciers, glacier landscapes, and glacier terminus regions.

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.

The thawing of permafrost on the Qinghai-Tibet Plateau (QTP) leads to more frequent occurrences of thaw slump (TS), which have significant impacts on local ecosystems, carbon cycles, and infrastructure development. Ac-curate recognition of TS would help in understanding its occurrence and evolution. Machine learning capabilities for TS recognition are still not fully exploited. We systematically evaluate the performance of machine learning models for TS recognition from unmanned aerial vehicle (UAV) and propose an ensemble learning object-based model for TS recognition (EOTSR). The EOTSR has the following advantages: 1) pioneering the introduction of spatial information to assist in recognition; 2) the misclassification of recognition models is improved by object -based technology; and 3) attempting to integrate the strengths of different machine learning models to obtain a recognition accuracy no less than that of commonly used deep learning models. The results show that object -based technology is more suitable for TS recognition than pixel-based technology. Recursive feature elimina-tion (RFE)-based feature selection proves that texture and geometry are effective complements to TS recognition. Among the improved object-based machine learning models, support vector machine (SVM) has the highest recognition accuracy, with an overall accuracy of 93.06 %. McNemar's test proves that EOTSR significantly improves TS recognition compared to a single model and achieves an overall accuracy of 97.32 %. The EOTSR model provides an effective recognition method for the increasingly frequent TS events in the permafrost regions of the QTP, and can produce label data for deep learning models based on satellite imagery.

Vegetation patch patterns, which are used as indicators of state, functionality, and catastrophic changes in the arid ecosystem, have received much attention. However, little is known about the controlling factors and indicators that underlie vegetation patch patterns in the alpine grassland ecosystem. Here, we firstly studied characteristics of vegetation patch patterns with aerial photography by using an unmanned aerial vehicle and evaluated the vegetation patch-size distribution with power law (PL) and truncated power law (TPL) models on the central part of the Qinghai-Tibetan Plateau (QTP). We then investigated the effects of environmental factors and biotic disturbances on vegetation patch patterns. The results showed that (1) there were four typical vegetation patch patterns, i.e. spot, stripe, sheet, and uniform patterns; (2) soil water content and air temperature were major environmental factors affecting vegetation patch patterns; (3) biotic disturbance of plateau pika (Ochotona curzoniae) affected vegetation patch patterns by changing the number, area, and connectivity of vegetation patches; and (4) vegetation patch-size distribution parameters were significantly related to soil hydrothermal variables (P < 0.01). We concluded that the development of alpine vegetation patch patterns was controlled by soil hydrothermal conditions and plateau pika's disturbance. We also proposed that gamma (TPL-PL) (difference between absolute values of slopes of TPL and PL curve fits) could serve as an effective indicator for monitoring alpine grassland conditions, and preventing patchiness was a critical task for the alpine ecosystem management and restoration.

Artificial glacier melt reduction is gaining increasing attention because of rapid glacier retreats and the projected acceleration of future mass losses. However, quantifying the effect of artificial melt reduction on glaciers in China has not been currently reported. Therefore, the case of Urumqi Glacier No.1 (eastern Tien Shan, China) is used to conduct a scientific evaluation of glacier cover efficiency for melt reduction between 24 June and 28 August 2021. By combining two high-resolution digital elevation models derived from terrestrial laser scanning and unmanned aerial vehicles, albedo, and meteorological data, glacier ablation mitigation under three different cover materials was assessed. The results revealed that up to 32% of mass loss was preserved in the protected areas compared with that of the unprotected areas. In contrast to the unprotected glacier surface, the nanofiber material reduced the glacier melt by up to 56%, which was significantly higher than that achieved by geotextiles (29%). This outcome could be attributed to the albedo of the materials and local climate factors. The nanofiber material showed higher albedo than the two geotextiles, dirty snow, clean ice, and dirty ice. Although clean snow had a higher albedo than the other materials, its impact on slowing glacier melt was minor due to the lower snowfall and relatively high air temperature after snowfall in the study area. This indicates that the efficiencies of nanofiber material and geotextiles can be beneficial in high-mountain areas. In general, the results of our study demonstrate that the high potential of glacier cover can help mitigate issues related to regions of higher glacier melt or lacking water resources, as well as tourist attractions.

High-resolution aerial images allow detailed analyses of periglacial landforms, which is of particular importance in light of climate change and resulting changes in active layer thickness. The aim of this study is to show possibilities of using UAV-based photography to perform spatial analysis of periglacial landforms on the Demay Point peninsula, King George Island, and hence to supplement previous geomorphological studies of the South Shetland Islands. Photogrammetric flights were performed using a PW-ZOOM fixed-winged unmanned aircraft vehicle. Digital elevation models (DEM) and maps of slope and contour lines were prepared in ESRI ArcGIS 10.3 with the Spatial Analyst extension, and three-dimensional visualizations in ESRI ArcScene 10.3 software. Careful interpretation of orthophoto and DEM, allowed us to vectorize polygons of landforms, such as (i) solifluction landforms (solifluction sheets, tongues, and lobes); (ii) scarps, taluses, and a protalus rampart; (iii) patterned ground (hummocks, sorted circles, stripes, nets and labyrinths, and nonsorted nets and stripes); (iv) coastal landforms (cliffs and beaches); (v) landslides and mud flows; and (vi) stone fields and bedrock outcrops. We conclude that geomorphological studies based on commonly accessible aerial and satellite images can underestimate the spatial extent of periglacial landforms and result in incomplete inventories. The PW-ZOOM UAV is well suited to gather detailed geomorphological data and can be used in spatial analysis of periglacial landforms in the Western Antarctic Peninsula region.

The introduction of cloud condensation nuclei and radiative heating by sunlight-absorbing aerosols can modify the thickness and coverage of low clouds, yielding significant radiative forcing of climate. The magnitude and sign of changes in cloud coverage and depth in response to changing aerosols are impacted by turbulent dynamics of the cloudy atmosphere, but integrated measurements of aerosol solar absorption and turbulent fluxes have not been reported thus far. Here we report such integrated measurements made from unmanned aerial vehicles (UAVs) during the CARDEX (Cloud Aerosol Radiative Forcing and Dynamics Experiment) investigation conducted over the northern Indian Ocean. The UAV and surface data reveal a reduction in turbulent kinetic energy in the surface mixed layer at the base of the atmosphere concurrent with an increase in absorbing black carbon aerosols. Polluted conditions coincide with a warmer and shallower surface mixed layer because of aerosol radiative heating and reduced turbulence. The polluted surface mixed layer was also observed to be more humid with higher relative humidity. Greater humidity enhances cloud development, as evidenced by polluted clouds that penetrate higher above the top of the surface mixed layer. Reduced entrainment of dry air into the surface layer from above the inversion capping the surface mixed layer, due to weaker turbulence, may contribute to higher relative humidity in the surface layer during polluted conditions. Measurements of turbulence are important for studies of aerosol effects on clouds. Moreover, reduced turbulence can exacerbate both the human health impacts of high concentrations of fine particles and conditions favorable for low-visibility fog events.