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Land-use–cover change (LUCC)/vegetation cover plays a critical role in Earth system science and is a reflection of human activities and environmental changes. LUCC will affect the structure and function of ecosystems and a series of other terrestrial surface processes, such as energy exchange, water circulation, biogeochemical circulation, and vegetation productivity. Therefore, accurate LUCC mapping and vegetation cover monitoring are the bases for simulating the global carbon and hydrological cycles, studying the interactions of the land surface and climate, and assessing land degradation. Based on field GPS surveys and UAV data, with cloud-free and snow/glacier algorithms and the SVM classifier to train and model alpine grassland, the alpine grassland and LUCC were extracted by using Landsat-8 OLI satellite images in Sanjiangyuan National Park in this paper. The latest datasets of vegetation types with 30 m × 30 m spatial resolution in the three parks were prepared and formed. The classification results show that the SVM classifier could better distinguish the major land-use types, and the overall classification accuracy was very high. However, in the alpine grassland subcategories, the classification accuracies of the four typical grasslands were relatively low, especially between desert steppes and alpine meadows, and desert steppes and alpine steppes. It manifests the limitations of Landsat-8 multispectral remote sensing imageries in finer-resolution grassland classifications of high-altitude alpine mountains. The method can be utilized for other multispectral satellite imageries with the same band matching, such as Landsat 7, Landsat 9, Sentinel-2, etc. The method described in this paper can rapidly and efficiently process annual alpine grassland maps of the source areas of the Yellow River, the Yangtze River, and the Lancang River. It can provide timely and high-spatial-resolution datasets for supporting scientific decisions for the sustainable management of Sanjiangyuan National Park. [ABSTRACT FROM AUTHOR]

Precipitation data from ground-based observatories in the Dongting Lake basin are often missing, resulting in large errors in surface precipitation data obtained by interpolation, which affects the accuracy of hydro-meteorological studies. Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) is the main high-resolution precipitation product, which is available to supplement measured missing data. To evaluate the applicability of this product in the Dongting Lake basin at multiple spatial and temporal scales, this paper analyzes daily, monthly, seasonal, annual, and extreme precipitation events of the three latest IMERG precipitation products (IPPs) (IMERG-F, IMERG-E, and IMERG-L) using eight statistical evaluation metrics. We find that the spatial and temporal performance of IMERG precipitation products varies over different time scales and topographic conditions. However, all three metrics (CC, RMSE, and RB) of the IMERG-F precipitation products outperform the IMERG-E and IMERG-L precipitation products for the same period. In the comparison of IMERG and TRMM (Tropical Rainfall Measuring Mission) precipitation products on monthly and seasonal scales, IMERG-F performed the best. IPPs can capture precipitation more accurately on seasonal scales and perform better in winter, indicating good detection of trace precipitation. Both high and low altitudes are not favorable for the satellite detection of extreme precipitation in both general and extreme precipitation events. Overall, the accuracy of IMERG-F with correction delay is slightly better than that of IMERG-E and IMERG-L without correction under near-real-time conditions, which is applicable in the Dongting Lake basin. However, the correction process also exacerbates overestimation of the precipitation extent. [ABSTRACT FROM AUTHOR]

As the most basic physical geographic elements, basin terrain and river networks have high spatial complexity and are closely related. However, there is little research on the correlation between terrain and river networks. In this paper, the Yellow River Basin was selected as the study area. Topographic factors of multiple dimensions were calculated. The influence of different topographic factors on the river network structure at different scales and their correlation from a multifractal perspective based on geographical detectors and a geographically weighted regression model were determined. The explanatory power of topography on the river network structure at different scales was: multifractal spectrum width > multifractal spectrum difference > slope > average elevation > elevation maximum > elevation minimum, which generally indicated that the topographic factor that has the greatest influence on the river network structure is the complexity and singularity of the terrain. The second-order clustering of regression coefficients from the results of the geographically weighted regression model revealed that the Yellow River basin was divided into three types of high-aggregation areas, which are dominated by the Qinghai-Tibet Plateau, the Loess Plateau, and the Huang-Huaihai Plain, respectively. The clustering results also revealed that the river network structure was affected by different key topographic factors in the different types of areas. This research studies and quantifies the relationship between basin topography and river network structure from a new perspective and provides a theoretical basis for unraveling the development of topography and river networks. [ABSTRACT FROM AUTHOR]

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method. [ABSTRACT FROM AUTHOR]

Green vegetation plays a vital role in urban ecosystem services. Rapid urbanization often tends to induce urban vegetation cover fragmentation (UVCF) in cities and suburbs. Mapping the changes in the structure (aggregation) and abundance of urban vegetation cover helps to make improved policies for sustainable urban development. In this paper, a new distance-based landscape indicator to UVCF, Frag, was proposed first. Unlike many other landscape indicators, Frag measures UVCF by considering simultaneously both the structure and abundance of vegetation cover at local scales, and thus provides a more comprehensive perspective in understanding the spatial distribution patterns in urban greenness cover. As a case study, the urban greenness fragmentation indicated by Frag was demonstrated in Wuhan metropolitan area (WMA), China in 2015 and 2020. Support vector machine (SVM) was then designed to examine the impact on the Frag changes from the associated factors, including urbanization and terrain characteristics (elevation and slope). The Frag changes were mapped at different scales and modeled by SVM from the selected factors, which reasonably explained the Frag changes. Sensitivity analysis for the SVM model revealed that urbanization showed the most dominant factor for the Frag changes, followed by terrain elevation and slope. We conclude that Frag is an effective scale-dependent indicator to UVCF that can reflect changes in the structure and abundance of urban vegetation cover, and that modeling the impact of the associated factors on UVCF via the Frag indicator can provide essential information for urban planners. [ABSTRACT FROM AUTHOR]

This paper presents a recent rainfall-induced landslide in China that occurred on August 21, 2020 and resulted in nine deaths. The sliding material traveled a distance of 800 m, with an altitude difference of about 180 m. A field investigation, remote sensing based on an unmanned aerial vehicle (UAV), in situ monitoring, and a rainfall data analysis were carried out to reveal the deposit characteristics, causative factors, post-landslide behavior, and the mechanism of the landslide. A saltatory micro-relief of the original slope determined the multiple-stage failure type of the slide, and also promoted the entrainment effect during the landslide movement. After the first-initiation sliding stage, the motion of this landslide involved typical progressive movement, and over time, the style of the runout generally turned into a flow-like form. Furthermore, the antecedent cumulative rainfall of 149.5 mm directly contributed to the occurrence of the landslide. Using the GB-SAR early warning system, the post-landslide residual failure was successfully predicted 10 min in advance. The combination of the UAV and GB-SAR technique can surely be beneficial for other inaccessible landslide investigations as well and improves the emergency rescue security. [ABSTRACT FROM AUTHOR]

The Mu Us dune field is one of China's four major dune fields, which are ecologically vulnerable areas of northwest semiarid land across Shaanxi, Ningxia, and Inner Mongolia, also very sensitive to the global temperature rise and environmental changes. This paper uses data on the temperature, precipitation, and precipitable water vapor (PWV) in the Mu Us dune field and its surrounding areas to analyze and discuss the time series and spatial distribution characteristics of these three factors in this area. The results of the study show that, in recent years, the trend of temperature increase in the Mu Us dune field has been higher than the average level in China, but this trend has gradually subsided since 2000. The spatial distribution of temperature presents an obvious characteristic of gradual increase from north to south and is affected by latitude, altitude, and topography. The annual cumulative precipitation of the Mu Us dune field is lower than the average level in China. However, in recent years, the rate of the increase in precipitation in this area has been significantly higher than that of the average rate of increase in China. The eastern part of the dune field has the most precipitation, which gradually decreases to the west. The spatial distribution of precipitation is greatly affected by monsoon factors in the region and the distribution of rivers. In the research field, PWV has been rising in recent years, which is greatly related to the increase of vegetation coverage in this region. This demonstrates that the Mu Us dune field has experienced a "warmer and wetter" trend in recent years. [ABSTRACT FROM AUTHOR]

Nighttime light data have been proven to be valuable for socioeconomic studies. However, they are not only affected by anthropogenic factors but also by physical factors, and previous studies have rarely examined these diverse variables in a systematic way that explains differences in nighttime lights across different cities. In this paper, hierarchical linear models at two levels of city and province were developed to investigate the nighttime lights effect on cross-level factors. An experiment was conducted for 281 prefecture cities in Mainland China using orbital satellite data in 2016. (1) There exist significant differences among city average lights, of which 49.9% is caused at the provincial level, indicating the factors at the provincial level cannot be ignored. (2) Economy-energy-infrastructure and demography factors have a significant positive lights effect. Meanwhile, industry-information and living-standard factors at the provincial level can further significantly increase these differences by 18.30% and 29.01%, respectively. (3) The natural-greenness factor displayed a significant negative lights effect, and its interaction with natural-ecology will continue to decrease city lights by 11.99%. However, artificial-greenness is an unreliable city-level factor explaining lights variations. (4) As for the negative lights effect of elevation and latitude, these become significant in a multivariate context and contribute lights indirectly. (5) The two-level hierarchical linear models are statistically significant at the level of 10%, and compared with the null model, the explained variances on city lights can be improved by 70% at the city level and 90% at the provincial level in the final mixed effect model. [ABSTRACT FROM AUTHOR]

The latest reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF), ERA5, can provide atmospheric data for calculating Zenith Tropospheric Delay (ZTD) with hourly temporal resolution, which is a key factor in Global Navigation Satellite System (GNSS) high-precision application. This paper is aimed at evaluating the performance of ZTD derived from ERA5 reanalysis data over China using 219 GNSS stations of the Crustal Movement Observation Network of China (CMONOC) covering the period from 2015 to 2016. The site-specific hourly ZTD at these stations is obtained by integration method and Saastamoinen model method on ERA5 pressure-level and surface-level reanalysis data with the temporal resolution of 1 h and the spatial resolution of 0.25° × 0.25°. Firstly, the atmospheric temperature and pressure that derived from ERA5 are compared with temperature and pressure obtained from meteorological sensors available at 193 GNSS stations. The biases are 2.31 °C and 1.26 mbar implying the accuracy and feasibility of ERA5 pressure and temperature for calculating ZTD over China. Secondly, the performance of ERA5 ZTD is systematically evaluated using ZTD from 219 GNSS sites. The average bias and Root Mean Square (RMS) of ERA5 pressure-level ZTD at all test stations in integration method are approximately 2.97 mm and 11.49 mm respectively, and those of ERA5 surface-level ZTD in model method are 7.97 mm, 39.25 mm, which indicates that ERA5 pressure-level ZTD has a higher accuracy over China. Further analysis indicates that the accuracies of ZTD derived from ERA5 pressure-level and surface-level data are approximately 13.8% and 10.9% higher than those from of ERA-Interim pressure-level and surface-level data. Moreover, ERA5 is able to accurately capture the short-term (hourly) variation of ZTD, which further indicates the better performance of ERA5. Thirdly, the temporal and spatial variation characteristics of ERA5 ZTD accuracy are further analyzed over China. The results show that the ZTD in the southern region has the lower accuracy compared with that in the northern region over China due to the influence of latitude and altitude. Furthermore, it is found that the ERA5 ZTD over China has obvious seasonality, with higher accuracy in winter and lower accuracy in summer. [ABSTRACT FROM AUTHOR]

Cloud is an important factor that affects weather and climate, and the vertical distribution of cloud determines its role in the atmospheric radiation transfer process. In this paper, the characteristics of different cloud types and their vertical cloud base height distributions over Eastern China are investigated with a four-year 2B-CLDCLASS-LIDAR product. The intercomparison of cloud base height distribution from ground-based lidar, CloudSat and CALIPSO measurements was studied with observations over the Hefei and Jinhua areas. The 2B-CLDCLASS-LIDAR product has the potential to uncover geographical and seasonal changes in cloud base height distribution over the Hefei area and Jinhua area, which may be beneficial for local climate models, although the CPR on CloudSat suffers from surface clutter or blind-zones. The results show that for non-precipitation cloud over the defined region (Eastern China), the occurrence frequencies of altocumulus, stratocumulus, and cirrus clouds are 29.4%, 21.0%, and 18.9%, respectively. The vertical occurrence frequencies of their cloud base heights are 0.5–8.5 km, below 3.5 km, and 5.5–17.0 km. The precipitation clouds are dominated by nimbostratus (48.4%), cumulus (17.9%), and deep convective clouds (24.2%), and their cloud base heights are all below 3.0 km. The cloud base height distributions have large differences below 3 km between the satellite measurement and ground-based measurement over Hefei site. Between the Hefei site and Jinhua site, the difference in cloud base height distribution measured by ground-based lidar is in good agreement with that measured by satellite over their matched grid boxes. Over the Hefei site, the vertical occurrence frequencies of cloud base height measured by ground-based lidar are higher than the satellite measurement within 0–0.5 km during all the seasons. It is suggested that more cloudy days may result from the sufficient water vapor environment in Hefei. In summer, the occurrence frequency of the cloud base height distribution at a height of 0–2.0 km is lower than other seasons over Jinhua city, which may be associated with the local weather system. Over the Jinhua site, the difference in seasonal cloud base height distribution based on satellite is in good agreement with that based on ground-based lidar. However, it does not appear over Hefei site. Thus, a multi-platform observation of cloud base height seems to be one of the essential ways for improvement in the observation of cloud macroscopic properties. [ABSTRACT FROM AUTHOR]