Global Land Surface Research Articles

A high temporal resolution global gridded dataset of human thermal stress metrics

The human thermal stress indices and datasets are vital for promoting public health and reducing negative environmental impacts as global climate change and extreme meteorological events increase. The current thermal indices generally use an instantaneous or average value to describe thermal stress which cannot reflect the distribution of thermal comfort conditions over time, and there are no global-scale thermal stress datasets with both 0.1° or higher spatial resolution and hourly temporal resolution available yet. A novel human thermal metric, Thermal Stress Duration (TSD), is proposed to represent the accumulative time of different thermal stress levels within a certain period. A high temporal resolution global gridded dataset of human thermal stress metrics (HiGTS) is presented, which consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily TSD at 0.1° × 0.1° spatial resolution over the global land surface, spanning from January 1, 2000, to December 31, 2023.

The Accelerating Loss of Resilience in Suburban Woodlands Can Largely Be Attributed to the Changes in Urban Precipitation Patterns.

Vegetation resilience holds significant importance for stabilizing ecosystem service functions in a changing climate. While global land surface vegetation resilience changes have been extensively studied, the impact of urbanization on the resilience of suburban woodlands remains inadequately understood. In this study, we utilized two critical slowing down (CSD) indicators, namely lag-one autocorrelation (LOA) and variance (VA), to assess the vegetation resilience, its long-term trends, and influencing factors in suburban woodlands across 1356 cities worldwide. The recovery rates estimated by LOA ( ) and VA ( ) showed close alignment in suburban woodlands with low suburban forest coverage (SFC) areas (correlation coefficient (r) = 0.95). However, a notable divergence was observed in areas with high SFC (r = 0.73). Suburban woodlands with high SFC typically exhibited lower recovery rate estimates, thus indicating greater vegetation resilience compared to areas with lower SFC. From 1986 to 2022, the recovery rates of suburban woodland areas in over 83% of the cities demonstrated a significant upward trend, with an average of 3.23 × 10-3 year-1 for both and , signifying a widespread decline in vegetation resilience. The accelerating pace of urbanization led to higher rising rates of and during 2010-2022 (5.11 × 10-3 year-1) compared to 1986-1999 (0.49 × 10-3 year-1). The notable decrease in resilience of forestland was primarily attributed to reduced precipitation in urban suburbs, which can be explained by urbanization-induced heat island and building barrier effects, causing a shift of precipitation center from urban suburbs to central cities. In summary, this study revealed that urbanization diminishes the vegetation resilience of urban suburban woodlands by altering urban precipitation patterns. These findings underscore the necessity of augmenting water availability in urban suburbs to restore resilience in these woodlands, thereby enhancing their ecosystem service value.

NOAAGlobalTemp version 6: An AI-based global surface temperature dataset

Abstract The National Oceanic and Atmospheric Administration (NOAA) Global Surface Temperature (NOAAGlobalTemp) dataset is widely used for scientific research, operational monitoring, and climate assessment activities. Aligning with NOAA’s mission values, NOAAGlobalTemp has been updated to version 6 (i.e., NGTv6), which includes two enhancements over its predecessor (NGTv5). The first enhancement is the expansion of the spatial coverage to encompass the entire globe and the extension of temporal coverage back to 1850 (an interim version of NOAAGlobalTemp with these features was released in February 2023). The expansion of spatial coverage is accomplished by utilizing surface air temperatures over the Arctic Ocean and by eliminating the data reconstruction mask used in NGTv5 that had suppressed interpolation in data-sparse regions. This change has important implications for global temperature trends since the Arctic region has been warming at a much faster pace, more than four times the global average, in the twenty-first century to date. The second enhancement is the implementation of a methodology based on artificial intelligence (AI) for reconstructing surface air temperature over the global land surface and the Arctic Ocean. The AI model employs an artificial neural network to fill data gaps and is demonstrated to be more robust, stable, and accurate than the previous gap-filling method, particularly in observation-sparse areas such as the polar regions. The model outperforms the previous approach across all evaluated statistical metrics, and the output reaches a stable state more quickly as observations are received, which facilitates climate monitoring.

Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information

Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (XGBoost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the XGBoost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R2 and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved XGBoost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias = −0.033), GLASS (RMSE = 0.089, bias = −0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias = −0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.

Evaluation of the Surface Downward Longwave Radiation Estimation Models over Land Surface

Surface downward longwave radiation (SDLR) is crucial for maintaining the global radiative budget balance. Due to their ease of practicality, SDLR parameterization models are widely used, making their objective evaluation essential. In this study, against comprehensive ground measurements collected from more than 300 globally distributed sites, four SDLR parameterization models, including three popular existing ones and a newly proposed model, were evaluated under clear- and cloudy-sky conditions at hourly (daytime and nighttime) and daily scales, respectively. The validation results indicated that the new model, namely the Peng model, originally proposed for SDLR estimation at the sea surface and applied for the first time to the land surface, outperformed all three existing models in nearly all cases, especially under cloudy-sky conditions. Moreover, the Peng model demonstrated robustness across various land cover types, elevation zones, and seasons. All four SDLR models outperformed the Global Land Surface Satellite product from Advanced Very High-Resolution Radiometer Data (GLASS-AVHRR), ERA5, and CERES_SYN1de-g_Ed4A products. The Peng model achieved the highest accuracy, with validated RMSE values of 13.552 and 14.055 W/m2 and biases of −0.25 and −0.025 W/m2 under clear- and cloudy-sky conditions at daily scale, respectively. Its superior performance can be attributed to the inclusion of two cloud parameters, total column cloud liquid water and ice water, besides the cloud fraction. However, the optimal combination of these three parameters may vary depending on specific cases. In addition, all SDLR models require improvements for wetlands, bare soil, ice-covered surfaces, and high-elevation regions. Overall, the Peng model demonstrates significant potential for widespread use in SDLR estimation for both land and sea surfaces.

Enhancing Evapotranspiration Estimations through Multi-Source Product Fusion in the Yellow River Basin, China

An accurate estimation of evapotranspiration (ET) is critical to understanding the water cycle in watersheds and promoting the sustainable utilization of water resources. Although there are various ET products in the Yellow River Basin, various ET products have many uncertainties due to input data, parameterization schemes, and scale conversion, resulting in significant uncertainties in regional ET data products. To reduce the uncertainty of a single product and obtain more accurate ET data, more accurate ET data can be obtained by fusing different ET data. Addressing this challenge, by calculating the uncertainty of three ET data products, namely global land surface satellite (GLASS) ET, Penman–Monteith–Leuning (PML)-V2 ET, and reliability-affordable averaging (REA) ET, the weight of each product is obtained to drive the Bayesian three-cornered Hat (BTCH) algorithm to obtain higher quality fused ET data, which are then validated at the site and basin scales, and the accuracy has significantly improved compared to a single input product. On a daily scale, the fused data’s root mean square error (RMSE) is 0.78 mm/day and 1.14 mm/day. The mean absolute error (MAE) is 0.53 mm/day and 0.84 mm/day, respectively, which has a lower RMSE and MAE than the model input data; the correlation coefficients (R) are 0.9 and 0.83, respectively. At the basin scale, the RMSE and MAE of the annual average ET of the fused data are 11.77 mm/year and 14.95 mm/year, respectively, and the correlation coefficient is 0.84. The results show that the BTCH ET fusion data are better than single-input product data. An analysis of the fused ET data on a spatiotemporal scale shows that from 2001 to 2017, the ET increased in 85.64% of the area of the Yellow River Basin. Fluctuations in ET were greater in the middle reaches of the Yellow River than in the upstream and downstream regions. The BTCH algorithm has indispensable reference value for regional ET estimation research, and the ET data after BTCH algorithm fusion have higher data quality than the original input data. The fused ET data can inform the development of management strategies for water resources in the YRB and provide a deeper understanding of the regional water supply and demand balance mechanism.

A Preliminary Study on the Whole-Plant Regulations of the Shrub Campylotropis polyantha in Response to Hostile Dryland Conditions.

Drylands cover more than 40% of global land surface and will continue to expand by 10% at the end of this century. Understanding the resistance mechanisms of native species is of particular importance for vegetation restoration and management in drylands. In the present study, metabolome of a dominant shrub Campylotropis polyantha in a dry-hot valley were investigated. Compared to plants grown at the wetter site, C. polyantha tended to slow down carbon (C) assimilation to prevent water loss concurrent with low foliar reactive oxygen species and sugar concentrations at the drier and hotter site. Nitrogen (N) assimilation and turn over were stimulated under stressful conditions and higher leaf N content was kept at the expense of root N pools. At the drier site, roots contained more water but less N compounds derived from the citric acid cycle. The site had little effect on metabolites partitioning between leaves and roots. Generally, roots contained more C but less N. Aromatic compounds were differently impacted by site conditions. The present study, for the first time, uncovers the apparent metabolic adaptations of C. polyantha to hostile dryland conditions. However, due to the limited number of samples, we are cautious about drawing general conclusions regarding the resistance mechanisms. Further studies with a broader spatial range and larger time scale are therefore recommended to provide more robust information for vegetation restoration and management in dryland areas under a changing climate.

Eco‐friendly Anode Materials for Lithium and Sodium‐ion Batteries from Wood Sources

AbstractThe intricate materials found in nature boast remarkable multifunctional properties honed through millions of years of evolution, resulting in the highest optimal organization in terms of function, structure, and chemistry. Leveraging the distinctive attributes of natural materials through biomimicry present a captivating avenue for research, brimming with vast potential for groundbreaking discoveries. Among the array of precursors available, wood stands out as a prominent candidate that covered over 30 % of the global land surface. Renowned for its captivating mechanical properties and anisotropic hierarchical porosity finely tuned to facilitate swift pathways, wood embodies attributes of abundance and biodegradability. Consequently, scientists have drawn inspiration from wood's exceptional characteristics to engineer batteries exhibiting remarkable electrochemical performance. For instance, the characteristic hierarchical multi‐channeled construction of wood serves as a blueprint for synthesizing energy storage materials endowed with heightened ion and electron diffusivity. Serving as an integrated carbonaceous scaffold featuring a hierarchical architecture and aligned channels, wood‐based anodes enhance ion and electron conductivities, while bolstering the kinetics of charge transfer in lithium‐ion batteries (LIBs) and sodium‐ion batteries (SIBs). This review underscores the recent strides made in utilizing wood as a hierarchically porous and renewable material for developing anode materials tailored for LIBs and SIBs. Additionally, it sheds light on the potential of wood‐derived anode materials for LIBs and SIBs to alleviate the strain on critical raw materials, while accentuating the additional environmental sustainability benefits derived from such innovations.

An evaluation of evapotranspiration products over the Tibetan Plateau

Abstract In situ observations from 13 sites located over the Tibetan Plateau (TP) are used to evaluate evapotranspiration (ET) products, including remote sensing-based, land surface modeled, and reanalysis products. It is found that the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the simulations by the Community Land Model - Dynamic Global Vegetation Model (CLM-BGCDV) are the top-rank products measured by the Percentage bias, Root Mean Square Error, and Correlation Coefficient against in-situ observations. The evaluated data are then used to examine the consistency in spatial and temporal variability of summer ET and its controlling factors on the TP and the Three-River Sources Region (TRSR). All products show consistently that precipitation in central semiarid part of TP is the dominant factor influencing summer ET, while air temperature plays a certain role in the southeastern and eastern TP. Uncertainties exist in western TP, possibly due to the lack of observations or gaps in the satellite data. Some suggestions for improving ET product development based on models and satellite retrievals, particularly for the cold and complex surface of the TP are also given.

Development of a Simple Observation System to Monitor Regional Wind Erosion

Dryland occupies about 46% of the global land surface area (except Antarctica) and is the most vulnerable area to climate change. From the conditions of vegetation and land surface wetness and blown sand phenomena, a simple observation system was developed to monitor regional wind erosion and applied to Khuld of Mongolia, which is sensitive to drought and desertification. The system was composed of instruments that observed blown sand, vegetation amount, land surface wetness, and landscape features related to regional wind erosion. Sixteen blown sand and eight sandstorm events were evaluated from 5 March to 5 June 2023 (i.e., during the Asian dust season in northeast Asia). The normalized difference vegetation index and visible images showed that the vegetation amount was considerably less, and the developed moisture index related to land surface wetness indicated dry conditions. Combining the results of blown sand, these indices, and visible images, land surface conditions during the analysis period were likely to occur with blown sand events.

Temporal Dynamics of Global Barren Areas between 2001 and 2022 Derived from MODIS Land Cover Products

Long-term monitoring studies on the transition of different land cover units to barren areas are crucial to gain a better understanding of the potential challenges and threats that land surface ecosystems face. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products (MCD12C1) to conduct geospatial analysis based on the maximum extent (MaxE) concept, to assess the spatiotemporal changes in barren areas from 2001 to 2022, at global and continental scales. The MaxE area includes all the pixels across the entire period of observations where the barren land cover class was at least once present. The relative expansion or reduction of the barren areas can be directly assessed with MaxE, as any annual change observed in the barren distribution is comparable over the entire dataset. The global barren areas without any land change (UA) during this period were equivalent to 12.8% (18,875,284 km2) of the global land surface area. Interannual land cover changes to barren areas occurred in an additional area of 3,438,959 km2 (2.3% of the global area). Globally, barren areas show a gradual reduction from 2001 (91.1% of MaxE) to 2012 (86.8%), followed by annual fluctuations until 2022 (88.1%). These areas were mainly interchanging between open shrublands and grasslands. A relatively high transition between barren areas and permanent snow and ice is found in Europe and North America. The results show a 3.7% decrease in global barren areas from 2001 to 2022. Areas that are predominantly not barren account for 30.6% of the transitional areas (TAs), meaning that these areas experienced short-term or very recent transitions from other land cover classes to barren. Emerging barren areas hotspots were mainly found in the Mangystau region (Kazakhstan), Tibetan plateau, northern Greenland, and the Atlas Mountains (Morocco, Tunisia).

Predictor Importance for Hydrological Fluxes of Global Hydrological and Land Surface Models

Predictor Importance for Hydrological Fluxes of Global Hydrological and Land Surface Models

Global reduction in sensitivity of vegetation water use efficiency to increasing CO2

Ecosystem water use efficiency (WUE), which measures the ratio of carbon gain to water loss, is crucial to the global water, energy, and carbon cycles. It is an important metric in understanding how efficiently an ecosystem uses water to assimilate carbon and is affected by various environmental factors, including CO2 levels. However, the temporal dynamics of CO2-induced changes in WUE (CO2_WUE) remain uncertain. After isolating CO2_WUE from total changes in WUE, we assessed historical changes in global CO2_WUE and future projected trends. We found that the increasing atmospheric CO2 concentration enhanced global WUE by 5.65 ± 1.14 % 100 ppm−1 during 1982–2015. However, CO2-driven gains in WUE exhibit a significant declining trend across most of the Earth’s land surface during 1982–2015 (−1.38 ± 0.19 % 100 ppm−1 yr−1). This widespread CO2_WUE decline is projected to continue throughout the remainder of the 21st century. The decline in CO2_WUE implies that the global land surface is likely to witness a higher water cost to maintain the land carbon sink. This finding suggests a heightened difficulty of mitigating global warming and an elevated risk of global water scarcity. The global CO2_WUE decline is primarily driven by rising vapor pressure deficit (VPD) reducing photosynthesis and increasing total evaporation. Our findings highlight the key role of VPD in the coupling of global water and carbon cycles and reveal a continuous increase in the water cost of the global land carbon sink.

Nine months of daily LiDAR, orthophotos and MetOcean data from the eroding soft cliff coast at Happisburgh, UK

The dynamic interaction between cliff, beach and shore-platform is key to assessing the sediment balance for coastal erosion risk assessments, but this is poorly understood. We present a dataset containing daily, 3D,colour LiDAR scans of a 450 m coastal section at Happisburgh, Norfolk, UK. This previously para-glaciated region comprises mixed sand-gravel sediments, which are less well-understood and well-studied than sandy beaches. From Apr-Dec 2019, 236 daily surveys were carried out. The dataset presented includes: survey areas, transects LiDAR scans, georeferenced orthophotos, meteorological- and oceanographical conditions during the Apr-Dec observation period. Full LiDAR point-clouds are available for 67 scans (Oct-Dec). Hourly time-series of offshore sea-state parameters (significant wave height, mean propagation direction, selected spectral periods) were obtained by downscaling the ERA5 global reanalysis data (global atmosphere, land surface and ocean waves) using the numerical model Simulating Waves Nearshore (SWAN). We indicate how to obtain hourly precipitation time-series by interpolating ERA5 data. This dataset is important for researchers understanding the interaction between cliff, beach and shore-platform in open-coast mixed-sand-gravel environments.

Emerging Methods to Validate Remotely Sensed Vegetation Water Content

AbstractSatellite‐retrieved vegetation optical depth (VOD) has provided extensive insights into global plant function (such as, carbon stocks, water stress, crop yields) because of VOD's ability to monitor plant water stress and biomass at near daily temporal frequency under all‐weather conditions. However, arguably, the greatest challenge with broadly applying VOD is its lack of validation partly because of VOD's simultaneous sensitivity to plant water status and biomass changes, as well as intensive methods required to measure these properties in‐situ. Here, inspired by the recent Yao et al. (2024), https://doi.org/10.1029/2023GL107121 article, I argue that VOD estimated from global navigation satellite systems (GNSS) and land surface models with plant hydraulic schemes are two emerging methods that show promise for more widely validating satellite‐based VOD. I encourage wider adoption of these approaches to validate and further advance satellite‐based VOD research.

Technical note: Surface fields for global environmental modelling

Abstract. Climate change has resulted in more frequent occurrences of extreme events, such as flooding and heavy snowfall, which can have a significant impact on densely populated or industrialised areas. Numerical models are used to simulate and predict these extreme events, enabling informed decision-making and planning to minimise human casualties and to protect costly infrastructure. LISFLOOD is an integrated hydrological model underpinning the European Flood Awareness System and Global Flood Awareness System (EFAS and GloFAS, respectively), developed by the Copernicus Emergency Management Service (CEMS). The CEMS_SurfaceFields_2022 dataset is a new set of high-resolution surface fields at 1 and 3 arcmin resolution (approximately 2 and 6 km at the Equator, respectively) based on a wide variety of high-resolution and up-to-date data sources. The 1 arcmin fields cover Europe, while the surface fields at 3 arcmin cover the global land surface (excluding Antarctica). The dataset encompasses (i) catchment morphology and river networks, (ii) land use, (iii) vegetation cover type and properties, (iv) soil properties, (v) lake information, and (vi) water demand. This paper details the complete workflow used to generate the CEMS_SurfaceFields_2022 fields, including the data sources and methodology. Whilst created together with upgrades to the open source LISFLOOD code, the CEMS_SurfaceFields_2022 fields can be used independently for a wide range of applications, including as input to hydrological, Earth system, or environmental models or for carrying out general analyses across spatial scales, ranging from global and regional levels to local levels (especially useful for regions outside Europe), expected to improve the accuracy, detail and realism of applications.

Analyzing Spatio-Temporal Dynamics of Grassland Resilience and Influencing Factors in the West Songnen Plain, China, for Eco-Restoration.

Grassland plays an indispensable role in the stability and development of terrestrial ecosystems. Quantitatively assessing grassland resilience is of great significance for conducting research on grassland ecosystems. However, the quantitative measurement of resilience is difficult, and research on the spatio-temporal variation of grassland resilience remains incomplete. Utilizing the Global Land Surface Satellite (GLASS) leaf area index (LAI) product derived from MODIS remote sensing data, along with land cover and meteorological data, this paper constructed the grassland resilience index (GRI) in the west Songnen Plain, China, a typical region with salt and alkali soils. This paper analyzed the spatio-temporal changes of the GRI and explored the contribution of climate factors, human activities, and geographical factors to the GRI. The results revealed that from 2000 to 2021, the GRI in the study area ranged from 0.1 to 0.22, with a multi-year average of 0.14. The average GRI exhibited a pattern of high-value aggregations in the north and low-value distributions in the south. Trend analysis indicated that areas with an improved GRI accounted for 59.09% of the total grassland area, but there were still some areas with serious degradation. From 2000 to 2015, the latitude and mean annual temperature (MAT) were principal factors to control the distribution of the GRI. In 2020, the mean annual precipitation (MAP) and MAT played important roles in the distribution of the GRI. From 2000 to 2021, the influence of human activities was consistently less significant compared to geographical location and climate variables.

A Cross-Resolution Surface Net Radiative Inversion Based on Transfer Learning Methods

Net radiation (Rn) is a key component of the Earth’s energy balance. With the rise of deep learning technology, remote sensing technology has made significant progress in the acquisition of large-scale surface parameters. However, the generally low spatial resolution of net radiation data and the relative scarcity of surface flux site data at home and abroad limit the potential of deep learning methods in constructing high spatial resolution net radiation models. To address this challenge, this study proposes an innovative approach of a multi-scale transfer learning framework, which assumes that composite models at different spatial scales are similar in structure and parameters, thus enabling the training of accurate high-resolution models using fewer samples. In this study, the Heihe River Basin was taken as the study area and the Rn products of the Global Land Surface Satellite (GLASS) were selected as the target for coarse model training. Based on the dense convolutional network (DenseNet) architecture, 25 deep learning models were constructed to learn the spatial and temporal distribution patterns of GLASS Rn products by combining multi-source data, and a 5 km coarse resolution net radiation model was trained. Subsequently, the parameters of the pre-trained coarse-resolution model were fine-tuned with a small amount of measured ground station data to achieve the transfer from the 5 km coarse-resolution model to the 1 km high-resolution model, and a daily high-resolution net radiation model with 1 km resolution for the Heihe River Basin was finally constructed. The results showed that the bias, R2, and RMSE of the high-resolution net radiation model obtained by transfer learning were 0.184 W/m2, 0.924, and 24.29 W/m2, respectively, which was better than those of the GLASS Rn products. The predicted values were highly correlated with the measured values at the stations and the fitted curves were closer to the measured values at the stations than those of the GLASS Rn products, which further demonstrated that the transfer learning method could capture the soil moisture and temporal variation of net radiation. Finally, the model was used to generate 1 km daily net radiation products for the Heihe River Basin in 2020. This study provides new perspectives and methods for future large-scale and long-time-series estimations of surface net radiation.

Colonization ability and uniformity of resources and environmental factors determine biological homogenization of soil protists in human land-use systems.

Humans have substantially transformed the global land surface, resulting in the decline in variation in biotic communities across scales, a phenomenon known as "biological homogenization." However, different biota are affected by biological homogenization to varying degrees, but this variation and the underlying mechanisms remain little studied, particularly in soil systems. To address this topic, we used metabarcoding to investigate the biogeography of soil protists and their prey/hosts (prokaryotes, fungi, and meso- and macrofauna) in three human land-use ecosystem types (farmlands, residential areas, and parks) and natural forest ecosystems across subtropical and temperate regions in China. Our results showed that the degree of community homogenization largely differed between taxa and functional groups of soil protists, and was strongly and positively linked to their colonization ability of human land-use systems. Removal analysis showed that the introduction of widespread, generalist taxa (OTUs, operational taxonomic units) rather than the loss of narrow-ranged, specialist OTUs was the major cause of biological homogenization. This increase in generalist OTUs seemingly alleviated the negative impact of land use on specialist taxa, but carried the risk of losing functional diversity. Finally, homogenization of prey/host biota and environmental conditions were also important drivers of biological homogenization in human land-use systems, with their importance being more pronounced in phagotrophic than parasitic and phototrophic protists. Overall, our study showed that the variation in biological homogenization strongly depends on the colonization ability of taxa in human land-use systems, but is also affected by the homogenization of resources and environmental conditions. Importantly, biological homogenization is not the major cause of the decline in the diversity of soil protists, and conservation and study efforts should target at taxa highly sensitive to local extinction, such as parasites.

Human activities and increased anthropogenic emissions: A remote sensing study in Cyprus

BackgroundThe effects of human activity on the environment are widespread and widely known. Human activities on most continents can be generalized as shelters and food chains due to the basic requirements of human life. Most of these activities require Land-use Land-cover (LULC) transformations, and their effects can be seen as changes such as increases in the global Land Surface Temperature (LST) and air pollutant concentrations. The present research aims to use remote sensing to monitor LULC transformations in Cyprus. MethodThis research uses Sentinel-3 data, Python programming, geographical information systems, and remote sensing to develop a moving average research method for a case study of Cyprus. Importantly, this work eliminates all political and ethnic boundaries to produce a unified analysis. ResultBased on the research outcomes, the highest mean LST and sulfur dioxide, nitrogen dioxide, and particulate matter (with a diameter of 10 μm or less) emissions occur in the Limassol, Famagusta, Nicosia, and Larnaca districts. These emissions are mainly attributable to artificial surfaces, agricultural areas, and forested and semi-natural areas. These trends may relate to electric power plants, a cement factory, an airport, and intensified agricultural activities in the research area.