China's largest freshwater lake, Poyang Lake, is well known for its ecological and economic importance as well as its rapid changes in lake inundation areas. However, due to technical difficulties, ...to date long-term records of its dynamic inundation areas are lacking, not to mention how they are affected by climate change and/or human activities. Using Moderate Resolution Imaging Spectroradiometer (MODIS) medium-resolution (250-m) data collected between 2000 and 2010 and an objective water/land delineation method, we documented and studied the short- and long-term characteristics of lake inundation. Significant seasonality and inter-annual variability were found in the monthly and annual mean inundation areas. The inundation area ranged between 714.1km2 in October 2009 and 3162.9km2 in August 2010, and the inundation area during any particular year could change by a factor of 2.3–3.2. During the 11-year period, the maximum possible inundation area was 14 times the minimum possible inundation area, indicating extreme variability. Both the annual mean and minimum inundation areas showed statistically significant declining trends from 2000 to 2010 (−30.2km2yr−1 and−23.9km2yr−1, p<0.05). The changes of the inundation area were primarily driven by local precipitation during non-summer months, while during summer months of July to September when the outflow into the Yangtze River was impeded the effect of precipitation became less significant. These results provide long-term baseline data to monitor future changes in Poyang Lake's inundation area in a timely fashion, for example quantifying the extreme drought conditions during spring 2011.
► Short- and long-term patterns of Poyang Lake's inundation were studied using MODIS. ► Significant seasonality and inter-annual inundation variability were found. ► Variation patterns were driven by precipitation and modulated by the Yangtze River.
Basin-scale water volumes of lakes and reservoirs are difficult to obtain due to a number of challenges. In this study, area-based water storage estimation models are proposed for large lakes and ...reservoirs in the Yangtze River Basin (YRB). The models are subsequently applied to Moderate Resolution Imaging Spectroradiometer (MODIS) observations of 128 large lakes and 108 reservoirs between 2000 and 2014, and the first comprehensive map of the temporal and spatial dynamics of water storage in large water bodies in the YRB is provided. The results show that 53.91% of the lakes experienced significant decreasing trends in water storage during this period, and the total water storage in lakes showed a decreasing trend of 14 million m
month
. By contrast, a monthly mean increase of 177 million m
was observed for water storage in reservoirs. Our analysis revealed that the pronounced increase in reservoirs was primarily due to the rapid water level increase in the Three Gorges Reservoir in recent years, while understanding the water loss in lakes requires additional studies. The long-term data presented in this study provide critical baseline information for future water resource monitoring and regulation in the YRB and China.
Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development ...of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.
Abstract
Lakes are important natural resources and carbon gas emitters and are undergoing rapid changes worldwide in response to climate change and human activities. A detailed global ...characterization of lakes and their long-term dynamics does not exist, which is however crucial for evaluating the associated impacts on water availability and carbon emissions. Here, we map 3.4 million lakes on a global scale, including their explicit maximum extents and probability-weighted area changes over the past four decades. From the beginning period (1984–1999) to the end (2010–2019), the lake area increased across all six continents analyzed, with a net change of +46,278 km
2
, and 56% of the expansion was attributed to reservoirs. Interestingly, although small lakes (<1 km
2
) accounted for just 15% of the global lake area, they dominated the variability in total lake size in half of the global inland lake regions. The identified lake area increase over time led to higher lacustrine carbon emissions, mostly attributed to small lakes. Our findings illustrate the emerging roles of small lakes in regulating not only local inland water variability, but also the global trends of surface water extent and carbon emissions.
Abstract
Quantifying the drivers of terrestrial vegetation dynamics is critical for monitoring ecosystem carbon sequestration and bioenergy production. Large scale vegetation dynamics can be observed ...using the leaf area index (LAI) derived from satellite data as a measure of ‘greenness’. Previous studies have quantified the effects of climate change and carbon dioxide (CO
2
) fertilization on vegetation greenness. In contrast, the specific roles of land-use-related drivers (LURDs) on vegetation greenness have not been characterized. Here, we combined the Interior-Point Method-optimized ecosystem model and the Bayesian model averaging statistical method to disentangle the roles of LURDs on vegetation greenness in China from 2000 to 2014. Results showed a significant increase in growing season LAI (greening) over 35% of the land area of China, whereas less than 6% of it exhibited a significantly decreasing trend (browning). The overall impact of LURDs on vegetation greenness over the whole country was comparatively low. However, the local effects of LURDs on the greenness trends of some specified areas were considerable due to afforestation and urbanization. Southern Coastal China had the greatest area fractions (35.82% of its corresponding area) of the LURDs effects on greening, following by Southwest China. It was because of these economic regions with great afforestation programs. Afforestation effects could explain 27% of the observed greening trends in the forest area. In contrast, the browning impact caused by urbanization was approximately three times of the greening effects of both climate change and CO
2
fertilization on the urban area. And they made the urban area had a 50% decrease in LAI. The effects of residual LURDs only accounted for less than 8% of the corresponding observed greenness changes. Such divergent roles would be valuable for understanding changes in local ecosystem functions and services under global environmental changes.
The two most common land cover types in urban areas, artificial surface (AS) and urban blue-green space (UBGS), interact with land surface temperature (LST) and exhibit competitive effects, namely, ...heating and cooling effects. Understanding the variation of these effects along the AS ratio gradient is highly important for the healthy development of cities. In this study, we aimed to find the critical point of the joint competitive effects of UBGS and AS on LST, and to explore the variability in different climate zones and cities at different development levels. An urban land cover map and LST distribution map were produced using Sentinel-2 images and Landsat-8 LST data, respectively, covering 28 major cities in China. On this basis, the characteristics of water, vegetation, and LST in these cities were analyzed. Moreover, the UBGS (water or vegetation)–AS–LST relationship of each city was quantitatively explored. The results showed that UBGS and AS have a competitive relationship and jointly affect LST; this competition has a critical point (threshold). When the proportion of UBGS exceeds this value, UBGS replaces AS as the dominant variable for LST, bringing about a cooling effect. In contrast, when AS dominates LST, it causes a warming effect. The critical points between AS and water and between AS and vegetation in 28 major cities in China were 80% and 70%, respectively. The critical point showed an obvious zonal difference. Compared with cities in subtropical and temperate climate regions, the critical point of arid cities is higher, and UBGS exhibited better performance at alleviating the urban thermal environment. The critical point of cities with higher development levels is lower than that of cities with lower development levels. Even areas with relatively low AS coverage are prone to high temperatures, and more attention should be paid to improving the coverage of UBGS. Our research results provide a reference for the more reasonable handling of the relationship between urban construction, landscape layout, and temperature control.
Satellite altimetry has been effectively used for monitoring lake level changes in recent years. This work focused on the integration of multiple satellite altimetry datasets from ICESat-1, Envisat ...and Cryosat-2 for the long-term (2002–2017) observation of lake level changes in the middle and lower Yangtze River Basin (MLYB). Inter-altimeter biases were estimated by using the gauged daily water level data. It showed that the average biases of ICESat-1 and Cryosat-2 with respect to Envisat were 6.7 cm and 3.1 cm, respectively. The satellite-derived water levels were evaluated against the gauged data. It indicated significantly high correlations between the two datasets, and the combination of three altimetry data produced precise water level time series with high temporal and spatial resolutions. A liner regression model was used to estimate the rates of lake level changes over the study period after the inter-altimeter bias adjustment was performed. The results indicated that ~79% of observed lakes (41/52) showed increasing trends in water levels with rates up to 0.203 m/y during 2002–2017. The temporal analysis of lake level variations suggested that ~60% of measured lakes (32/53) showed decreasing trends during 2002–2009 while ~66% of measured lakes (79/119) exhibited increasing trends during 2010–2017. Most of measured reservoirs displayed rapidly rising trends during the study period. The driving force analysis indicated that the temporal heterogeneity of precipitation can be mainly used to explain the observed pattern of lake level changes. The operation of reservoirs and human water consumption were also responsible for the lake level variations. This work demonstrated the potential of integrating multiple satellite altimeters for the long-term monitoring of lake levels, which can help to evaluate the impact of climate change and anthropogenic activities on regional water resources.
The ongoing Russia-Ukraine war has an impact on air quality in the contested region, but it is difficult to assess it during the war and to distinguish between weather conditions and anthropogenic ...impacts on air pollution. Aerosol optical depth (AOD) might be a way to indicate the regional air quality remotely. In this study, we analyze satellite-based MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD products to compare monthly mean AOD values before and during the war. By examining the spatial-temporal distribution of AOD in 2022 and comparing it with a baseline period from 2012 to 2021, we aim to assess the impact of the conflict. Our analysis employs a time series algorithm that decomposes long-term trends and seasonal variations, enabling us to identify AOD changes associated with the war-induced artificial perturbations. Additionally, we utilize satellite-based tropospheric NO2 data and nighttime lighting data as auxiliary sources to support the analysis of abnormal AOD changes resulting from the war. Furthermore, four parameters from Aerosol Robotic Network (AERONET) measurements in the Kyiv site, including angstrom exponent (AE), single scattering albedo (SSA), refractive index (RI), fine mode fraction (FMF) were exclusively discussed to explore the possible changes of aerosol physical-optical properties over Ukraine during the war. Results showed that air quality in Ukraine has been affected by the war in contradictory ways at different levels. At the national level, atmospheric pollution has dropped across Ukraine due to a decrease in the sources of air pollution emissions as a result of the suppression of economic and agricultural activities. Meanwhile, atmospheric air quality has deteriorated at the local scale where war is intense due to the large amounts of air pollutants emitted by explosions. Specifically, a significant decrease in AOD of 22.46% compared to the baseline period was observed across Ukraine in January and February 2022 before the war. Additionally, there was a column density reduction of over 10% for tropospheric NO2 compared to the same period in the previous year. Following the outbreak of the war on February 24, significant increases in AOD were observed in March when compared to the baseline period. Eastern Ukraine experienced a significant increase of 40.14%, while northern Ukraine and central Ukraine saw rises of 34.15% and 45.78%, respectively. In April, a wider distribution of areas with significantly high AOD values across Ukraine was observed compared to March. In July, widespread areas with anomalous low AOD appeared in northern and western Ukraine, likely due to a decrease in post-harvest agricultural burning caused by the war. In August, anomalous high AOD areas were observed in central Ukraine, possibly attributed to continued Russian long-range strikes and a new round of bombardments.
•Air quality in Ukraine was affected by the war in contradictory ways at the national and local levels.•Abnormally low AOD occurred at the national scale due to decreased industrial production and agricultural burning.•Abnormally high AOD was observed at the local scale where the war was intense.•Use MAIAC AOD to assess the war objectively and provide consistency checks with previous studies using sample data.
Wetland vegetation is an important component of wetland ecosystems and plays a crucial role in the ecological functions of wetland environments. Accurate distribution mapping and dynamic change ...monitoring of vegetation are essential for wetland conservation and restoration. The development of unoccupied aerial vehicles (UAVs) provides an efficient and economic platform for wetland vegetation classification. In this study, we evaluated the feasibility of RGB imagery obtained from the DJI Mavic Pro for wetland vegetation classification at the species level, with a specific application to Honghu, which is listed as a wetland of international importance. A total of ten object-based image analysis (OBIA) scenarios were designed to assess the contribution of five machine learning algorithms to the classification accuracy, including Bayes, K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and random forest (RF), multi-feature combinations and feature selection implemented by the recursive feature elimination algorithm (RFE). The overall accuracy and kappa coefficient were compared to determine the optimal classification method. The main results are as follows: (1) RF showed the best performance among the five machine learning algorithms, with an overall accuracy of 89.76% and kappa coefficient of 0.88 when using 53 features (including spectral features (RGB bands), height information, vegetation indices, texture features, and geometric features) for wetland vegetation classification. (2) The RF model constructed by only spectral features showed poor classification results, with an overall accuracy of 73.66% and kappa coefficient of 0.70. By adding height information, VIs, texture features, and geometric features to construct the RF model layer by layer, the overall accuracy was improved by 8.78%, 3.41%, 2.93%, and 0.98%, respectively, demonstrating the importance of multi-feature combinations. (3) The contribution of different types of features to the RF model was not equal, and the height information was the most important for wetland vegetation classification, followed by the vegetation indices. (4) The RFE algorithm effectively reduced the number of original features from 53 to 36, generating an optimal feature subset for wetland vegetation classification. The RF based on the feature selection result of RFE (RF-RFE) had the best performance in ten scenarios, and provided an overall accuracy of 90.73%, which was 0.97% higher than the RF without feature selection. The results illustrate that the combination of UAV-based RGB imagery and the OBIA approach provides a straightforward, yet powerful, approach for high-precision wetland vegetation classification at the species level, in spite of limited spectral information. Compared with satellite data or UAVs equipped with other types of sensors, UAVs with RGB cameras are more cost efficient and convenient for wetland vegetation monitoring and mapping.
High tibial osteotomy (HTO) has been widely used for clinical treatment of osteoarthritis of the medial compartment of the knee, and both opening-wedge and closing-wedge HTO are the most commonly ...used methods. However, it remains unclear which technique has better clinical and radiological outcomes in practice. To systematically evaluate this issue, we conducted a comprehensive meta-analysis by pooling all available data for the opening-wedge HTO and closing-wedge HTO techniques from the electronic databases including PubMed, Embase, Wed of Science and Cochrane Library. A total of 22 studies encompassing 2582 cases were finally enrolled in the meta-analysis. There was no significant difference regarding surgery time, duration of hospitalization, knee pain VAS, Lysholm score and HSS knee score (clinical outcomes) between the opening-wedge and closing-wedge HTO groups (P > 0.05). However, the opening-wedge HTO group showed wider range of motion than the closing-wedge HTO group (P = 0.003). Moreover, as for Hip-Knee-Ankle angle and mean angle of correction, no significant difference was observed between the opening-wedge and closing-wedge HTO groups (P > 0.05), while the opening-wedge HTO group showed greater posterior tibial slope angle (P < 0.001) and lesser patellar height than the closing-wedge HTO group (P < 0.001). On light of the above analysis, we believe that individualized surgical approach should be introduced based on the clinical characteristics of each patient.
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