Trophically integrated ecometric models as tools for demonstrating spatial and temporal functional changes in mammal communities.

We are in a modern biodiversity crisis that will restructure community compositions and ecological functions globally. Large mammals, important contributors to ecosystem function, have been affected directly by purposeful extermination and indirectly by climate and land-use changes, yet functional turnover is rarely assessed on a global scale using metrics based on functional traits. Using ecometrics, the study of functional trait distributions and functional turnover, we examine the relationship between vegetation cover and locomotor traits for artiodactyl and carnivoran communities. We show that the ability to detect a functional relationship is strengthened when locomotor traits of both primary consumers (artiodactyls, n = 157 species) and secondary consumers (carnivorans, n = 138 species) are combined into one trophically integrated ecometric model. Overall, locomotor traits of 81% of communities accurately estimate vegetation cover, establishing the advantage of trophically integrated ecometric models over single-group models (58 to 65% correct). We develop an innovative approach within the ecometrics framework, using ecometric anomalies to evaluate mismatches in model estimates and observed values and provide more nuance for understanding relationships between functional traits and vegetation cover. We apply our integrated model to five paleontological sites to illustrate mismatches in the past and today and to demonstrate the utility of the model for paleovegetation interpretations. Observed changes in community traits and their associated vegetations across space and over time demonstrate the strong, rapid effect of environmental filtering on community traits. Ultimately, our trophically integrated ecometric model captures the cascading interactions between taxa, traits, and changing environments.

Wildflower plantings enhance nesting opportunities for soil-nesting bees.

Ongoing declines of bees and other pollinators are driven in part by the loss of critical floral resources and nesting substrates. Most conservation/restoration efforts for bees aim to enhance floral abundance and continuity but often assume the same actions will bolster nesting opportunities. Recent research suggests that habitat plantings may not always provide both forage and nesting resources. We evaluated wildflower plantings designed to augment floral resources to determine their ability to enhance nesting by soil-nesting bees over 3 study years in Northern California agricultural landscapes. We established wildflower plantings along borders of annual row crops and paired each with an unplanted control border. We used soil emergence traps to assess nest densities and species richness of soil-nesting bees from spring through late summer at paired field borders planted with wildflowers or maintained conventionally as bare or sparsely vegetated areas, as is typical for the region. We also quantified soil-surface characteristics and flower resources among borders. Wildflower plantings significantly increased nest densities and the richness of bee species using them. Such benefits occurred within the first year of planting and persisted up to 4 years post establishment. The composition of nesting bee communities also differed between wildflower and unenhanced borders. Wildflower plantings differed from controls in multiple characteristics of the soil surface, including vegetation cover, surface microtopography and hardness. Surprisingly, only vegetation cover significantly affected nest densities and species richness. Wildflower plantings are a widespread habitat action with the potential to support wild bees. The demonstrated benefit wildflower plantings had for increasing the nesting of soil-nesting bees greatly augments their relevance for the conservation of wild bee communities in agricultural and other landscapes. Identifying soil-surface characteristics that are important for nesting provides critical information to guide the implementation and management of habitats for bees.

Assessing the effects of climate and human activity on vegetation change in Northern China.

Fractional vegetation cover (FVC) has changed significantly under various disturbances over northern China in recent decades. This research examines the dynamics of FVC and how it is affected by climate and human activity during the period of 1990-2018 in northern China. The effects of climate change (i.e., temperature, precipitation, solar radiation, and soil moisture) and human activity (socioeconomic data and land use) on vegetation coverage change in northern China from 1990 to 2018 were quantified using the Sen + Mann-Kendall test, partial correlation analysis, and structural equation modelling (SEM) methods. The findings of this research indicate the following: (1) From 1990 to 2018, the overall trend in FVC in northern China was increased. The areas with obvious increases were mainly situated on the northern slope of Tianshan Mountains, Xinjiang, the Loess Plateau, the Northeast China Plain, and the Sanjiang Plain, while the areas with distinct degradation were located in the Inner Mongolia Plateau, the Changbai Mountain and the eastern part of north China. (2) In the past 29 years, the FVC in northern China has been mainly affected by precipitation and soil moisture. (3) Based on structural equation modelling, we discovered that certain variables impacted the main factors influencing the amount of FVC in northern China. Human activity has had a larger impact on FVC than climate change. Our findings can accelerate the comprehension of vegetation dynamics and their underlying mechanisms and provide a theoretical basis for regional ecological environmental protection.

Effects of coal mining and climate-environment factors on the evolution of a typical Eurasian grassland.

Coal mining can significantly impact vegetation evolution, yet the limited information on its patterns and driving factors hampers efforts to mitigate these effects and reclaim abandoned mines. This study aimed to 1) examine vegetation evolution in a semiarid steppe watershed in northeast China; and 2) characterize the driving factors behind this evolution. We analyzed the impact of twelve selected driving factors on fractional vegetation coverage (FVC) from 2000 to 2021 using a dimidiate pixel model, Sen's slope analysis, Mann-Kendall trend test, coefficient of variation analysis, and Geodetector model. At a significance level of α = 0.05, our findings revealed a south-to-north decline pattern in FVC, a significant decrease trend in proximity to coal mines, and a notable increase trend adjacent to river channels. Approximately 37% of the watershed exhibited low FVC, while the overall temporal trend across the watershed was deemed insignificant. Areas surrounding the mines experienced a substantial reduction in FVC due to coal mining activities, while FVC variations across the watershed were linked to precipitation, temperature, and soil type. FVC predictions improved notably when interactions between multiple two-way factors were considered. Each driving factors displayed an optimal range (e.g., precipitation = 63-71 mm) for maximizing FVC. Given the study watershed's status as a national energy base, understanding vegetation responses to coal mining and climate-environment changes is crucial for sustaining fragile terrestrial ecosystems and socioeconomic development. Achieving a long-time balance between coal extraction and ecological protection is essential. The study outcomes hold significant promise for advancing ecological conservation, vegetation restoration, and mitigation of environmental degradation in semiarid regions affected by extensive coal mining and climate fluctuations. These findings contribute to the strategic management of such areas, promoting sustainable practices amidst evolving environmental challenges.

Image-based vegetation analysis of desertified area by using a combination of ImageJ and Photoshop software.

Fractional vegetation cover (FVC) is a crucial indicator to estimate degradation and desertification for grasslands. However, traditional small-scale FVC analysis methods, such as visual estimation and point-sampling, are cumbersome and imprecise. Innovative methods like image-based FVC analysis methods, while accurate, face challenges such as complex analytical procedures and the necessary training for operations. Therefore, in this study, a combined application of ImageJ and Photoshop was employed to achieve a more effective analysis of FVC values in desertification areas. Our results showed that the FVC results obtained by combination of Photoshop and ImageJ were dependable and precise (R2 > 0.98), demonstrating equivalency to results obtained through either visual estimation or Photoshop-based methods. Furthermore, even in the face of background interference and varied shooting angles, the combination of ImageJ and Photoshop software was still able to maintain a low error rate when analyzing FVC values (average error rate = - 2.6%). In conclusion, the imaged-based combined FVC analysis method employed in our research was an effective, precise, and efficient technique for analyzing small-scale FVC, promising substantial improvement over conventional methods.

Optimality principles explaining divergent responses of alpine vegetation to environmental change.

Recent increases in vegetation greenness over much of the world reflect increasing CO2 globally and warming in cold areas. However, the strength of the response to both CO2 and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite-observed peak season absorbed photosynthetically active radiation (Fmax ) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year-1 in drier regions and a browning of 0.12 ± 0.08% year-1 in wetter regions). Using an eco-evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in Fmax with a correlation coefficient (r) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year-1 ) and browning (0.07 ± 0.06% year-1 ). We also predicted the observed reduced sensitivities of Fmax to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces Fmax in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO2 stimulates vegetation growth by enhancing water-use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change.

Mine land reclamation, mine land reuse, and vegetation cover change: An intriguing case study in Dartford, the United Kingdom.

Dartford, a town in England, heavily relied on industrial production, particularly mining, which caused significant environmental pollution and geological damage. However, in recent years, several companies have collaborated under the guidance of the local authorities to reclaim the abandoned mine land in Dartford and develop it into homes, known as the Ebbsfleet Garden City project. This project is highly innovative as it not only focuses on environmental management but also provides potential economic benefits, employment opportunities, builds a sustainable and interconnected community, fosters urban development and brings people closer together. This paper presents a fascinating case that employs satellite imagery, statistical data, and Fractional Vegetation Cover (FVC) calculations to analyse the re-vegetation progress of Dartford and the development of the Ebbsfleet Garden City project. The findings indicate that Dartford has successfully reclaimed and re-vegetated the mine land, maintaining a high vegetation cover level while the Ebbsfleet Garden City project has advanced. This suggests that Dartford is committed to environmental management and sustainable development while pursuing construction projects.

Remote Sensing Image Characteristics and Typical Area Analysis of Taiyuan Xishan Ecological Restoration Area.

The Taiyuan Xishan Ecological Restoration Zone is located in the west of Taiyuan City and belongs to the Xishan Coalfield. Due to the resource development activity of coal mining, which is caused by coal gangue accumulation, surface vegetation degradation, bare surfaces, and other phenomena, it is most common in this area. These have an impact on the surface ecology; however, after ecological restoration, the surface ecology has been greatly improved. There are many extraction models of vegetation coverage based on pixel dichotomology combined with multispectral vegetation index, but we believe that the combination of visible light vegetation index to construct models is relatively unexplored. The main problem of how to use the RGB image data in order to quickly and accurately extract vegetation coverage information is still under investigation and needs researchers' attention. In this paper, through selecting the vegetation coverage as the evaluation index of ecological restoration effect, a new RGB vegetation coverage CIVE calculation model is innovatively proposed to solve the above problem, and on the basis of this model, the vegetation cover change analysis is carried out in the Xishan ecological restoration area of Taiyuan. According to the analysis of vegetation coverage change, relevant paper data, and the characteristics of multiple historical remote sensing images, the ecological restoration area of Taiyuan Xishan is divided into six typical areas. Through empirical evaluation, we summarize and analyze these six typical areas, which can provide typical demonstration roles for other ecological restoration areas. Our findings suggest that the proposed CIVE model realizes the extraction of vegetation cover information and long-term series dynamic monitoring of vegetation coverage.

Static Positioning under Tree Canopy Using Low-Cost GNSS Receivers and Adapted RTKLIB Software.

The decrease in costs and dimensions of GNSS receivers has enabled their adoption for a very wide range of users. Formerly mediocre positioning performance is benefiting from recent technology advances, namely the adoption of multi-constellation, multi-frequency receivers. In our study, we evaluate signal characteristics and horizontal accuracies achievable with two low-cost receivers-a Google Pixel 5 smartphone and a u-Blox ZED F9P standalone receiver. The considered conditions include open area with nearly optimal signal reception, but also locations with differing amounts of tree canopy. GNSS data were acquired using ten 20 min observations under leaf-on and leaf-off conditions. Post-processing in static mode was conducted using the Demo5 fork of the RTKLIB open source software, which is adapted for usage with lower quality measurement data. The F9P receiver provided consistent results with sub-decimeter median horizontal errors even under tree canopy. The errors for the Pixel 5 smartphone were under 0.5 m under open-sky conditions and around 1.5 m under vegetation canopy. The adaptation of the post-processing software to lower quality data was proven crucial, especially for the smartphone. In terms of signal quality (carrier-to-noise density, multipath), the standalone receiver provided significantly better data than the smartphone.

Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region.

The Silk Road Economic Belt and the 21st Century Maritime Silk Road Initiative (BRI) proposed in 2013 by China has greatly accelerated the social and economic development of the countries along the Belt and Road (B&R) region. However, the international community has questioned its impact on the ecological environment and a comprehensive assessment of ecosystem quality changes is lacking. Therefore, this study proposes an objective and automatic method to assess ecosystem quality and analyzes the spatiotemporal changes in the B&R region. First, an ecosystem quality index (EQI) is established by integrating the vegetation status derived from three remote sensing ecological parameters including the leaf area index, fractional vegetation cover and gross primary productivity. Then, the EQI values are automatically categorized into five ecosystem quality levels including excellent, good, moderate, low and poor to illustrate their spatiotemporal changes from the years 2016 to 2020. The results indicate that the spatial distributions of the EQIs across the B&R region exhibited similar patterns in the years 2016 and 2020. The regions with excellent levels accounted for the lowest proportion of less than 12%, while regions with moderate, low and poor levels accounted for more than 68% of the study area. Moreover, based on the EQI pattern analysis between the years 2016 and 2020, the regions with no significant EQI change accounted for up to 99.33% and approximately 0.45% experienced a significantly decreased EQI. Therefore, this study indicates that the ecosystem quality of the B&R region was relatively poor and experienced no significant change in the five years after the implementation of the "Vision and Action to Promote the Joint Construction of the Silk Road Economic Belt and the 21st Century Maritime Silk Road". This study can provide useful information for decision support on the future ecological environment management and sustainable development of the B&R region.

Soil fire severity is more relevant than fire frequency in explaining soil, carbon and nitrogen losses and vegetation recovery after wildfire in NW Spain.

NW Spain is one of the regions of Europe most affected by rural fires and where there is a particularly high risk of increased soil erosion after fire. An increase in fire frequency is expected to enhance soil erosion and the associated carbon and nitrogen losses, impairing vegetation recovery and compromising ecosystem resilience. In this study, the influence of recurrent fires on soil erosion, carbon and nitrogen loss as well as on vegetation recovery was assessed in four shrubland areas dominated by Erica australis L. Pterospartum tridentatum (L.) Willk. And Ulex gallii Planch, burned in October 2017. Two of the areas were burned twice, between 2010 and 2017, and the other two areas were burned once, in 2017. Soil burn severity was moderate to high in all experimental sites. Soil erosion along with vegetation cover and diversity were monitored during the two years after fire, on 24 plots of 80 m2. In the first year after fire, the mean sediment yield was 24.1 Mg ha-1 in the areas burned twice and 17.4 Mg ha-1 in the areas burned once. Fire frequency did not significantly influenced soil loss unlike the carbon and nitrogen concentrations in eroded sediments. Sediment losses as well as carbon and nitrogen losses were significantly associated with soil burn severity. Vegetation recovery was not affected by fire frequency in the shrublands, which were dominated by resprouters. No alteration in species composition was observed, indicating the high degree of resilience of the communities. In summary, recurrent fires occurring within an interval of 10 years seemed to have little effect on sediment yield and vegetation recovery. The significant influence of soil burn severity on sediment yield and vegetation recovery highlighted the importance of considering this factor in fire prevention plans for fire-prone areas.

Predicting community traits along an alpine grassland transect using field imaging spectroscopy.

Assessing plant community traits is important for understanding how terrestrial ecosystems respond and adapt to global climate change. Field hyperspectral remote sensing is effective for quantitatively estimating vegetation properties in most terrestrial ecosystems, although it remains to be tested in areas with dwarf and sparse vegetation, such as the Tibetan Plateau. We measured canopy reflectance in the Tibetan Plateau using a handheld imaging spectrometer and conducted plant community investigations along an alpine grassland transect. We estimated community structural and functional traits, as well as community function based on a field survey and laboratory analysis using 14 spectral vegetation indices (VIs) derived from hyperspectral images. We quantified the contributions of environmental drivers, VIs, and community traits to community function by structural equation modelling (SEM). Univariate linear regression analysis showed that plant community traits are best predicted by the normalized difference vegetation index, enhanced vegetation index, and simple ratio. Structural equation modelling showed that VIs and community traits positively affected community function, whereas environmental drivers and specific leaf area had the opposite effect. Additionally, VIs integrated with environmental drivers were indirectly linked to community function by characterizing the variations in community structural and functional traits. This study demonstrates that community-level spectral reflectance will help scale plant trait information measured at the leaf level to larger-scale ecological processes. Field imaging spectroscopy represents a promising tool to predict the responses of alpine grassland communities to climate change.

Efficiency of large-scale aided phytostabilization in a mining pond.

Mining activities accumulate large quantities of waste in tailing ponds, which results in several environmental impacts. In Cartagena-La Unión mining district (SE Spain), a field experiment was carried out in a tailing pond to evaluate the effect of aided phytostabilization on reducing the bioavailability of zinc (Zn), lead (Pb), copper (Cu) and cadmium (Cd) and enhancing soil quality. Nine native plant species were planted, and pig manure and slurry along with marble waste were used as amendments. After 3 years, the vegetation developed heterogeneously on the pond surface. In order to evaluate the factors affecting this inequality, four areas with different VC and an area without treatment (control area) were sampled. Soil physicochemical properties, total, bioavailable and soluble metals, and metal sequential extraction were determined. Results revealed that pH, organic carbon, calcium carbonate equivalent and total nitrogen increased after the aided phytostabilization, while electrical conductivity, total sulfur and bioavailable metals significantly decreased. In addition, results indicated that differences in VC among sampled areas were mainly owing to differences in pH, EC and concentration of soluble metals, which in turn were modified by the effect of non-restored areas on close restored areas after heavy rains due to a lower elevation of the restored areas compared to the unrestored ones. Therefore, to achieve the most favorable and sustainable long-term results of aided phytostabilization, along with plant species and amendments, micro-topography should be also taken into consideration, which causes different soil characteristics and thus different plant growth and survival.

Representativeness of the particulate matter pollution assessed by an official monitoring station of air quality in Santiago, Chile: projection to human health.

Santiago, capital city of Chile, presents air pollution problems for decades mainly by particulate matter, which significantly affects population health, despite national authority efforts to improve air quality. Different properties of the particulate matter (PM10, PM2.5 and PM1 fractions, particle surface and number) were measured with an optical spectrometer. The sampling was done during spring 2019 at different sites within the official representative area of Independencia monitoring station (ORMS-IS). The results of this study evidence large variations in PM mass concentration at small-scale areas within the ORMS-IS representative zone, which reports the same value for the total area. Results from PM properties such as PM1, particle number and particle surface distribution show that these properties should be incorporated in regular monitoring in order to improve the understanding of the effects of these factors on human health. The use of urban-climate canopy-layer models in a portion of the sampled area around the monitoring station demonstrates the influence of street geometry, building densities and vegetation covers on wind velocity and direction. These factors, consequently, have an effect on the potential for air pollutants concentrations. The results of this study evidence the existence of hot spots of PM pollution within the area of representativeness of the ORMS-IS. This result is relevant from the point of view of human health and contributes to improve the effectiveness of emission reduction policies.

An assessment of vegetation cover of Mysuru City, Karnataka State, India, using deep convolutional neural networks.

Mysuru City is a unique place in India due to its culture, green cover, historical places, and pleasant weather. In the last few decades, the city was witnessed rapid urban growth. This present work is conducted to assess the decadal changes in Mysuru City vegetation cover using multispectral remotely sensed data of 2009 and 2019 within Mysuru City Corporation (MCC). The main objective of this work is to assess the vegetation cover of the city and generate the land use and land cover classes (LULC) map using the deep learning model. Therefore, convolutional neural network (CNN)-based multiple training round (CNN-MTR) deep learning model is proposed and used for the classification of remote sensing images. The classified results were analyzed to assess the vegetation cover changes in the city over one decade. Vegetation cover within the Mysuru City Corporation area was estimated in 2019 to be 39.09% as compared to 43.32% in 2009. These results indicate that over a decade vegetation cover of Mysuru City is decreased by 3.43%. The overall classification accuracy of the proposed CNN-MTR model was estimated to be 95.20% for 2009 and 94.17% for 2019 respectively.

Mapping and dynamic monitoring of military training-induced vegetation cover loss using Sentinel-2 images and method comparison.

Sustainable management of the US Army installations is critical for military training and readiness of forces. However, monitoring military training-induced vegetation cover disturbances using remote sensing data is challenging due to the lack of methodology for optimizing the selection of spectral variables or predictors and spatial modeling methods. This study aimed to propose and demonstrate a methodological solution for this purpose. The study was conducted in the Fort Riley installation in which three training areas were selected to map and monitor the training-induced vegetation cover loss. Sentinel-2 images and field observations of percentage vegetation cover (PVC) were combined at a spatial resolution of 10 m by 10 m to map PVC and its dynamics by comparison of two predictor selection methods and five spatial modeling algorithms based on a total of 304 spectral variables from the images before and after the training. Results showed that overall, the correlation-based predictor selection method reduced the relative root mean square error (RRMSE) of PVC predictions by 4.44% than the random forest (RF)-based predictor selection. Machine learning methods including RF, neural network, and support vector machine overall reduced the RRMSE of PVC predictions by 42.83% compared with multiple linear regression and k-nearest neighbors. Combining correlation-based predictor selection and RF modeling, coupled with leave one out cross validation, provided the greatest potential of increasing the accuracy of monitoring the vegetation cover loss. The findings provided useful implications to develop a near real-time system of monitoring military training-induced vegetation cover loss.

Spatial and temporal variations of vegetation cover and its influencing factors in Shandong Province based on GEE.

Economic development has rapidly progressed since the implementation of reform and opening up policies, posing significant challenges to sustainable development, especially to vegetation, which plays a crucial role in maintaining ecosystem service functions and promoting green low-carbon transformations. In this study, we estimated the fractional vegetation cover (FVC) in Shandong Province from 2000 to 2020 using the Google Earth Engine (GEE) platform. The spatial and temporal changes in FVC were analyzed using gravity center migration analysis, trend analysis, and geographic detector, and the vegetation changes of different land use types were analyzed to reveal the internal driving mechanism of FVC changes. Our results indicate that vegetation cover in Shandong Province was in good condition during the period 2000 to 2020. The high vegetation cover classes dominated, and overall changes were relatively small, with the center of gravity of vegetation cover generally shifting towards the southwest. Land use type, soil type, population density, and GDP factors had the most significant impact on vegetation cover change in Shandong Province. The interaction of these factors enhanced the effect on vegetation cover change, with land use type and soil type having the highest degree of influence. The observational results of this study can provide data support for the policy makers to formulate new ecological restoration strategies, and the findings would help facilitate the sustainability management of regional ecosystem and natural resource planning.

Assessing the Impact of Wildlife on Vegetation Cover Change, Northeast Namibia, Based on MODIS Satellite Imagery (2002-2021).

Human-wildlife conflict in the Zambezi region of northeast Namibia is well documented, but the impact of wildlife (e.g., elephants) on vegetation cover change has not been adequately addressed. Here, we assessed human-wildlife interaction and impact on vegetation cover change. We analyzed the 250 m MODIS and ERA5 0.25° × 0.25° drone and GPS-collar datasets. We used Time Series Segmented Residual Trends (TSS-RESTREND), Mann-Kendall Test Statistics, Sen's Slope, ensemble, Kernel Density Estimation (KDE), and Pearson correlation methods. Our results revealed (i) widespread vegetation browning along elephant migration routes and within National Parks, (ii) Pearson correlation (p-value = 5.5 × 10-8) showed that vegetation browning areas do not sustain high population densities of elephants. Currently, the Zambezi has about 12,008 elephants while these numbers were 1468, 7950, and 5242 in 1989, 1994, and 2005, respectively, (iii) settlements and artificial barriers have a negative impact on wildlife movement, driving vegetation browning, and (iv) vegetation greening was found mostly within communal areas where intensive farming and cattle grazing is a common practice. The findings of this study will serve as a reference for policy and decision makers. Future studies should consider integrating higher resolution multi-platform datasets for detailed micro analysis and mapping of vegetation cover change.

Evaluation of dicamba volatilization when mixed with glyphosate using imazethapyr as a tracer.

Expansion of dicamba-resistant crops increased the frequency of off-target movement issues, especially in the midsouthern United States. Six field trials were conducted over two growing seasons with the purpose to determine the contribution of volatilization and physical suspension of particles to the off-target movement of dicamba when applied with glyphosate and imazethapyr - a non-volatile herbicide used as a tracer for physical off-target movement. Applications included dicamba at 560 g ha-1, glyphosate at 1260 g ha-1, and imazethapyr at 105 g ha-1. Applicators include glyphosate with dicamba to increase the spectrum of weed control from these applications; however, this addition increases potential for dicamba volatilization. Following application of the mixture, air samplers were placed in the field to collect dicamba and imazethapyr. Results showed there was at least 50 times more dicamba than imazethapyr detected even though the dicamba:imazethapyr ratio applied was 5.3:1. Dicamba was detected in the treated area and the off-site locations and all intervals of air sampling, ranging from 126 to 5990 ng. No more than 37.5 ng of imazethapyr was detected during the first 24-h after application (HAA) inside the treated area. Imazethapyr was only detected in 9 of the 20 sampling combinations during these experiments, and most of these detections (6) occurred during the first 24 HAA and inside the treated area. While some movement from the suspension of particles occurred based on the detection of imazethapyr in air samples, results show that most dicamba detection was due to the volatilization of the herbicide.

Solar photovoltaic program helps turn deserts green in China: Evidence from satellite monitoring.

Solar energy is considered one of the key solutions to the growing demand for energy and to reducing greenhouse gas emissions. Thanks to the relatively low cost of land use for solar energy and high power generation potential, a large number of photovoltaic (PV) power stations have been established in desert areas around the world. Despite the contribution to easing the energy crisis and combating climate change, large-scale construction and operation of PV power stations can change the land cover and affect the environment. However, few studies have focused on these special land cover changes, especially vegetation cover changes, which hinders understanding the effects of the extensive development of solar energy. Here, we used Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) based on Landsat images to monitor changes in vegetation abundance before and after the PV power stations deployment. To reduce the interference of PV shading on vegetation abundance estimation, we improved the vegetation (VG) fraction from SMA and developed the Photovoltaics-Adjusted Vegetation (PAVG) fraction for vegetation abundance measurements in PV power stations. Results show that PV power stations in China's 12 biggest deserts expanded from 0 to 102.56 km2 from 2011 to 2018, mainly distributed in the central part of north China. The desert vegetation in the deployment area of PV power stations presented a significant greening trend. Compared to 2010, the greening area reached 30.80 km2, accounting for 30% of the total area of PV power stations. Overall, the large-scale deployment of PV power stations has promoted desert greening, primarily due to government-led Photovoltaic Desert Control Projects and favorable climatic change. This study shows the great benefits of PV power stations in combating desertification and improving people's welfare, which bring sustainable economic, ecological and social prosperity in sandy ecosystems.