Global Environmental Monitoring Research Articles

A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices

The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.

Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas.

A data-driven approach for prioritising microbial and chemical hazards associated with dairy products using open-source databases

This study presents a data-driven approach for classifying food safety alerts related to chemical and microbial contaminants in dairy products using the Rapid Alert System for Food and Feed (RASFF) and the World Health Organization (WHO)’s Global Environmental Monitoring System (GEMS) food contaminants databases. This research aimed to prioritise microbial and chemical hazards based on their presence and severity through exploratory data analysis and to classify the severity of chemical hazards using machine learning (ML) approaches. It identified Listeria monocytogenes, Escherichia coli, Salmonella, Pseudomonas spp., Staphylococcus spp., Bacillus cereus, Clostridium spp., and Cronobacter sakazakii as the microbial hazards of priority in dairy products. The study also prioritised the top ten chemical hazards based on their presence and severity. These hazards include nitrate, nitrite, ergocornine, 3-MCPD ester, lead, arsenic, ochratoxin A, cadmium, mercury, and aflatoxin (G1, B1, G2, B2, G5 and M1). Using ML techniques, the accuracy rate of classifying food safety alerts as either ‘serious' or ‘non-serious' was up to 98 %. Additionally, the study identified Reference dose (RfD), substance amount, notification type, product, and substance as the most important features affecting the ML models' performance. These ML models (decision trees, random forests, k-nearest neighbors, linear discriminant analysis, and support vector machines) were also validated on an external dataset of RASFF alerts related to chemical contaminants in dairy products. They achieved an accuracy of up to 95.1 %. The study's findings demonstrate the models' robustness and ability to classify food safety alerts related to chemical contaminants in dairy products, even on new data. These results can enhance the development of more effective machine-learning models for classifying food safety alerts related to chemical contaminants in dairy products, highlighting the importance of developing accurate and efficient classification models for timely intervention.

Detection the Impact of Chlorophyll Index and Global Environmental Monitoring Index on Water Separation in Swansea in Wales, United Kingdom Through Analysing the Spectral Wavelengths of Landsat 8-OLI

Detection the Impact of Chlorophyll Index and Global Environmental Monitoring Index on Water Separation in Swansea in Wales, United Kingdom Through Analysing the Spectral Wavelengths of Landsat 8-OLI

Comparison of spatiotemporal burial and contamination of heavy metals in core sediments of two plateau lakes with contrasting environments: implication for anthropogenic-driven processes.

Investigating the impacts of climatic factors and human activities on sedimentary records of heavy metal (HM) contamination in lakes is essential for decision-making in global environmental monitoring and assessment. Spatiotemporal distributions of grain size (GS) and HM (Al, Cr, Mn, Ni, Cu, Zn, As, and Pb) concentrations have been conducted in core sediments that are collected from two adjacent plateau fault-bound lakes in southwest China with contrasting environments, i.e., deep oligotrophic Lake Fuxian (FX) and shallow hypertrophic Lake Xingyun (XY). Results showed that the average value of d50 in FX (4.61 μm) was lower than that in XY (8.35 μm), but the average concentrations of HMs (except Cr and Mn) in XY were higher than those in FX. Heavy metal burial rates (HMBR) were mainly controlled by sediment accumulation rates (SARs) rather than HM concentrations. The correlation coefficients between GS and HM concentrations became strong as the increasing water depths were associated with a stable sedimentary environment. Time-integrated enrichment factors (EF) and source identification of HMs between FX and XY represented that Cr, Ni, and Cu originated from natural sources but Mn, Zn, As, and Pb from anthropogenic sources, respectively. Regardless of FX and XY, the transition times of HMs from natural to anthropogenic sources occurred in the mid-1960s. Comparison of qualification impacts of climatic factors and human-induced factors on increased anthropogenic HMBR by the partial least squares path modeling (PLS-PM) implied that socio-economic activities, such as population density (PD) and gross domestic product (GDP), provided higher contributors to increased anthropogenic HMBR in XY (0.23/0.71) than FX (0.11/0.18). The comparative results of this study provided new insights into environmental monitoring and management of HM contamination for adjacent lakes with contrasting environments.

Does income inequality worsen pollution? The GEMS air pollution data revisited

Research on the effect of income inequality on pollution shows mixed results. This paper takes a new look at the urban air pollution data set of the U.N.‘s Global Environmental Monitoring System (GEMS). We investigate the impact of income inequality on urban air pollution and relate the results to a median-voter model. In this model, more income inequality decreases the median income, reduces pollution controls, and increases output and pollution when the median income is above a threshold. We find that income inequality, measured by the Gini coefficient, increases SO2 concentration in the rich democracies. The estimated effects are non-negligible in size. For the poor non-democracies, we find no evidence that the Gini coefficient impacts SO2 concentration. The Gini coefficient is estimated to increase the smoke concentration at the five- or ten-percent significance level in most specifications in a pooled sample. We find no evidence that the Gini coefficient impacts the concentration of particulates in a pooled sample. We conclude that the empirical results largely are consistent with the median-voter model.

Global monitoring of soil multifunctionality in drylands using satellite imagery and field data

AbstractModels derived from satellite image data are needed to monitor the status of terrestrial ecosystems across large spatial scales. However, a remote sensing‐based approach to quantify soil multifunctionality at the global scale is missing despite significant research efforts on this topic. A major constraint for doing so is the availability of suitable global‐scale field data to calibrate remote sensing indicators (RSI) and, to a lesser extent, the sensitivity of spectral data of available satellite sensors to soil background and atmospheric conditions. Here, we aimed to develop a soil multifunctionality model to monitor global drylands coupling ground data on 14 soil functions of 222 dryland areas from six continents to 18 RSI derived from a time series (2006–2013) Landsat dataset. Among the RSI evaluated, the chlorophyll absorption ratio index was the best predictor of soil multifunctionality in single‐variable‐based models (r = 0.66, P < 0.01, NMRSE = 0.17). However, a multi‐variable RSI model combining the chlorophyll absorption ratio index, the global environment monitoring index and the canopy‐air temperature difference improved the accuracy of quantifying soil multifunctionality (r = 0.73, P < 0.01, NMRSE = 0.15). Furthermore, the correlation between RSI and soil variables shows a wide range of accuracy with upper and lower values obtained for AMI (r = 0.889, NMRSE = 0.05) and BGL (r = 0.685, NMRSE = 0.18) respectively. Our results provide new insights on assessing soil multifunctionality using RSI that may help to monitor temporal changes in the functioning of global drylands effectively.

An intelligent modeling framework to optimize the spatial layout of ocean moored buoy observing networks

This research is motivated by the practical requirements in the sustainable deployment of ocean moored buoy observing networks. Ocean moored buoys play an important role in the global marine environment monitoring. Ocean buoy station layout planning is a typical multiple-objective spatial optimization problem that aims to reduce the spatial correlation of buoy stations and improve their spatial monitoring efficiency. In this paper, we develop a multi-objective mathematical model for allocating ocean buoy stations (MOLMofOBS) based on Tobler’s first law of geography. A spatial neighborhood model based on a Voronoi diagram is built to represent the spatial proximity of distributed buoy stations and delimit the effective monitoring region of every station. Then, a heuristic method based on a multiple-objective particles swarm optimization (MOPSO) algorithm is developed to calculate the MOLMofOBS via a dynamic inertia weight strategy. Meanwhile, a series of experiments is conducted to verify the efficiency of the proposed model and algorithms in solving single- and multiple-buoy station location problems. Finally, an interactive portal is developed in the Cyberinfrastructure environment to provide decision-making services for online real-time planning of the ocean buoy station locations. The work reported in this paper will provide spatial decision-making support for the sustainable development of ocean buoy observing networks.

Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

This paper addresses the issue of the satellite image processing using GRASS GIS in the mangrove forests of the Niger River Delta, southern Nigeria. The estuary of the Niger River Delta in the Gulf of Guinea is an essential hotspot of biodiversity on the western coast of Africa. At the same time, climate issues and anthropogenic factors affect vulnerable coastal ecosystems and result in the rapid decline of mangrove habitats. This motivates monitoring of the vegetation patterns using advanced cartographic methods and data analysis. As a response to this need, this study aimed to calculate and map several vegetation indices (VI) using scripts as advanced programming methods integrated in geospatial studies. The data include four Landsat 8-9 OLI/TIRS images covering the western segment of the Niger River Delta in the Bight of Benin for 2013, 2015, 2021, and 2022. The techniques included the ’i.vi’, ’i.landsat.toar’ and other modules of the GRASS GIS. Based on the GRASS GIS ’i.vi’ module, ten VI were computed and mapped for the western segment of the Niger River Delta estuary: Atmospherically Resistant Vegetation Index (ARVI), Green Atmospherically Resistant Vegetation Index (GARI), Green Vegetation Index (GVI), Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), Global Environmental Monitoring Index (GEMI), Normalized Difference Water Index (NDWI), Second Modified Soil Adjusted Vegetation Index (MSAVI2), Infrared Percentage Vegetation Index (IPVI), and Enhanced Vegetation Index (EVI). The results showed variations in the vegetation patterns in mangrove habitats situated in the Niger River Delta over the last decade as well as the increase in urban areas (Onitsha, Sapele, Warri and Benin City) and settlements in the Delta State due to urbanization. The advanced techniques of the GRASS GIS of satellite image processing and analysis enabled us to identify and visualize changes in vegetation patterns. The technical excellence of the GRASS GIS in image processing and analysis was demonstrated in the scripts used in this study.

Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors

The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple products using different models, but there are still significant differences between these products. This study generated a global GPP dataset (NI-LUE GPP) with 0.05° spatial resolution and at 8 day-intervals from 2001 to 2018 based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen (N) index. To simulate the global GPP, we mapped the global optimal ecosystem temperatures (Topteco) using satellite-retrieved solar-induced chlorophyll fluorescence (SIF) and applied it to calculate temperature stress. In addition, green chlorophyll index (CIgreen), which had a strong correlation with the measured canopy N concentrations (r = 0.82), was selected as the vegetation index to characterize the canopy N concentrations to calculate the spatiotemporal dynamic maximum light use efficiency (εmax). Multiple existing global GPP datasets were used for comparison. Verified by FLUXNET GPP, our product performed well on daily and yearly scales. NI-LUE GPP indicated that the mean global annual GPP is 129.69 ± 3.11 Pg C with an increasing trend of 0.53 Pg C/yr from 2001 to 2018. By calculating the SPAtial Efficiency (SPAEF) with other products, we found that NI-LUE GPP has good spatial consistency, which indicated that our product has a reasonable spatial pattern. This product provides a reliable and alternative dataset for large-scale carbon cycle research and monitoring long-term GPP variations.

Recent Development of Dielectric Elastomer Transducers and Potential Applications

Power generation using dielectric elastomer transducers is cheap, light, stackable, easy to install, and highly efficient. Also, since the dielectric elastomer transducer is an actuator developed into an artificial muscle, if the DE motor is further developed, it might be possibly be able to drive a vehicle. Efficient robot driving, various industrial machines and the use of dielectric elastomer sensors to optimize the driving may also help solve the above problems from the perspective of eco-driving. This paper describes the latest level of development of dielectric elastomers, their main problems and solutions to these problems, and their potential applications. The possibilities and concrete plans for building local global smart cities (including local generation power for local consumption), efficient transportation, and environmental monitoring systems utilizing dielectric elastomers are also discussed.

The impact of agricultural transformation on water quality in a data-scarce, dryland landscape-a case study in the Bot River, South Africa.

Transformation of natural vegetation to cultivated fields has resulted in marked increases in water quality degradation and nutrient loading of rivers globally. In many developing countries, monitoring and evaluating the impacts of agriculture on water quality are limited by financial constraints and focus is given to large water bodies. This paper presents and discusses the results of a year-long monitoring of a typical river system in an agricultural setting, namely the Bot River, Western Cape, South Africa. Results show seasonal increases in N concentrations and SRP driven by surrounding agricultural activities. Water chemistry and changes to nutrient loads were found to be site specific, which demonstrates that monitoring programmes focussing on one or two sites are not representative of the entire catchment. Monitoring and reporting of small river systems are thus un(der)-represented in large databases such as the UN Global Environment Monitoring System for Freshwater (GEMS/Water) programme. The results highlight the importance of selecting appropriate and representative monitoring sites for these rivers when budgetary constraints limit the number of points that can be monitored sustainably. The findings should also be applicable to similar catchments in the Western Cape and beyond as they demonstrate the magnitude of seasonal nutrient fluxes in the system.

GEMI – A Possible Tool for Identification of Disturbances in Confirerous Forests in Pernik Povince (Western Bulgaria)

Abstract The Global Environmental Monitoring Index (GEMI) represents a vegetation index that allows for making analysis. The index is not that sensitive to atmospheric effects. GEMI has been applied for the investigation of disruptions in the coniferous forests in Pernik Province, which is situated in the western parts of Bulgaria. The basic data comes from Landsat 8 and Corine Land Cover. The results of the study show that the index performs well in the distinguishment of broad-leaved vegetation from the coniferous one. At the same time the index doesn’t always provide satisfying results when it comes to deforestation. In conclusion GEMI provides good results, yet it’s use should be controlled and supported by other vegetation indices.

A Novel Jamming Method against SAR Using Nonlinear Frequency Modulation Waveform with Very High Sidelobes

Synthetic aperture radar (SAR) systems have the capacity for day-and-night and all-weather surveillance, which has become increasingly indispensable for military surveillance and global comprehensive environmental monitoring. With the development of the high-resolution SAR imaging technique, studies on SAR jamming have also received much interest. Traditional jamming methods are based on linear-frequency-modulated (LFM) signals, and this method can achieve high main-lobe jamming gain. However, its sidelobe jamming energy is very low. To solve this issue, a novel nonlinear frequency modulation (NLFM) waveform design method for SAR jamming is proposed in this paper. Compared with LFM waveforms, the designed waveforms have the same main-lobe jamming gain and very high sidelobes, which can significantly improve jamming performance. Moreover, detailed simulation experiments were carried out to verify the effectiveness of the newly proposed jamming scheme.

Corn grain yield forecasting by satellite remote sensing and machine‐learning models

AbstractThis study aimed to evaluate the performance of six machine‐learning models in forecasting corn (Zea mays L.) grain yield before harvest using, as input, variables in the models, some of the most‐used vegetation indices (VIs) and spectral bands in the literature, as well as using data at 770 and 980 sum of degree days (SDD). The field study was carried out in a commercial area in the 2017–2018 and 2018–2019 harvests. Spectral data were obtained from Sentinel‐2 satellite images and were used as input variables in the proposed models: artificial neural networks (ANN), k‐nearest neighbors (KNN), random forest (RF), and support vector machine (SVM). The maximum R2 and minimum values of mean absolute error (MAE) and RMSE were 0.89, 0.33, and 0.42 t ha−1, respectively, for the RF algorithm using all input variables. The results obtained in the present study show that it is possible to predict corn grain yield 80 d before harvest using only VIs for the crop. Testing the various combinations of spectral bands and VIs resulted in obtaining the GREEN band and the VI global environment monitoring index (GEMI) as the best predictor variables in the present study. The use of more than one SDD did not improve the performance of the models tested. The models developed using data at 980 SDD obtained the best precision and accuracy performance both in the scenario with all model input variables and with the two best predictors. The KNN algorithm obtained the best performance in the precision and accuracy metrics for most of the scenarios studied in the present work.

DIGITAL SOBRIETY: SOLUTION FOR DIGITAL POLLUTION ANDA TOOL FOR PROMOTING ENVIRONMENTAL SUSTAINABILITY

The recent global changes in the environment around us imply that the physical and digital worlds are becoming increasingly impacting. Digital technologies are progressively influencing industrial, agricultural, scientific, medical, educational, and other operations, as more and more aspects of daily life shift online. The application of environmental digital technologies in the context of global and national environmental monitoring, improving the safety of industrial firm operations, and enhancing environmental sustainability is a pressing necessity on the planet. Digital sobriety can be a solution for reducing digital pollution by requiring all digital players, whether customers, industry professionals, or public actors, to adjust their behaviour in order to assure the development of a sober, responsible, and ecologically ethical digital industry.

FIRE SEVERITY ASSESSMENT OF AN ALPINE FOREST FIRE WITH SENTINEL-2 IMAGERY

Abstract. Fire is a common phenomenon in many forests and is considered an important ecological tool. Fire severity mapping presents an effective way to assess post-fire management intervention and is helpful in environmental and climate change research. The objective of this study was to determine the severity of a forest fire event that occurred from 24th to 27th October 2019 at Taibon Agordino using Sentinel-2A satellite images and creating a severity map suitable as a decision-making tool for post-fire management intervention. The Sentinel-2A satellite data was classified into the following five classes: Unburned, Low Severity, Moderate Severity, High Severity, and Shadow with the non-parametric Random Forest (RF) classifier, and the resulting classified image was validated using validation sites. The RF classifier was applied first to the ten original band reflectance of Sentinel-2. In a second step, additional variables were added to the classification, namely the digital elevation model (DEM), the slope, and five vegetation indices (i.e., Differenced Normalized Burn Ratio (dNBR), Relative Differenced Normalized Burn Ratio (RdNBR), Differenced Bare Soil Index (dBSI), Global Environmental Monitoring Index (GEMI) and Burn Area Index (BAI)) The inclusion of vegetation indices and DEM-related variables increased the classification accuracy from 99.26% to 99.61% and the overall accuracy from 70.51% to 83.33%. In the classification with the ten original band reflectance, the variable of importance plot ranked the Red-Edge-3, Red, and SWIR 1 band reflectance as the top three most important input features, while for the classification with 17 variables, RdNBR, DEM and dNBR were the top three most important input features.

Continuous Change Detection and Classification—Spectral Trajectory Breakpoint Recognition for Forest Monitoring

Forest is one of the most important surface coverage types. Monitoring its dynamics is of great significance in global ecological environment monitoring and global carbon circulation research. Forest monitoring based on Landsat time-series stacks is a research hotspot, and continuous change detection is a novel approach to real-time change detection. Here, we present an approach, continuous change detection and classification-spectral trajectory breakpoint recognition, running on Google Earth Engine (GEE) for monitoring forest disturbance and forest long-term trends. We used this approach to monitor forest disturbance and the change in forest cover rate from 1987 to 2020 in Nanning City, China. The high-resolution Google Earth images are collected for the validation of forest disturbance. The classification accuracy of forest, non-forest, and water maps by using the optima classification features was 95.16%. For disturbance detection, the accuracy of our map was 86.4%, significantly higher than 60% of the global forest change product. Our approach can successfully generate high-accuracy classification maps at any time and detect the forest disturbance time on a monthly scale, accurately capturing the thinning cycle of plantations, which earlier studies failed to estimate. All the research work is integrated into GEE to promote the use of the approach on a global scale.

Siamese Detail Difference and Self-Inverse Network for Forest Cover Change Extraction Based on Landsat 8 OLI Satellite Images

In the context of carbon neutrality, forest cover change detection has become a key topic of global environmental monitoring. As a large-scale monitoring technique, remote sensing has received obvious attention in various land cover observation applications. With the rapid development of deep learning, remote sensing change detection combined with deep neural network has achieved high accuracy. In this paper, the deep neural network is used to study forest cover change with Landsat images. The main research ideas are as follows. (1) A Siamese detail difference neural network is proposed, which uses a combination of concatenate weight sharing mode and subtract weight sharing mode to improve the accuracy of forest cover change detection. (2) The self-inverse network is introduced to detect the change of forest increase by using the sample data set of forest decrease, which realizes the transfer learning of the sample data set and improves the utilization rate of the sample data set. The experimental results on Landsat 8 images show that the proposed method outperforms several Siamese neural network methods in forest cover change extraction.

TO THE ACTUALIZATION OF STIMULATION OF ACTIVITIES IN THE FIELD OF CLIMATE STUDY ON THE EXAMPLE OF ENSURING THE CAREER GROWTH OF THE CARETAKERS OF THE EKATERINBURG MAGNETIC AND METEOROLOGICAL OBSERVATORY (BY THE 185TH ANNIVERSARY OF OBSERVATORY OBSERVATIONS IN THE URALS)

For the first time for the 185th anniversary of the environmental observations in the Urals, information about the first caretakers Ekaterinburg Magnetic and Meteorological Observatory (EMMO) is presented. This is based on domestic and foreign archival data currently available on the world Internet. It is shown that the first caretakers have passed a wonderful way of life. All have made a great track record in the Mining Department, to which EMMO was originally attributed. They reached the general's ranks in the Rank Tables. On the scientific side, some of them became professors of the Mining Institute and members of the Scientific Committee of the Corps of Mountain Engineers. Some gaps have been restored in the biographies of Julius Reinke, Ivan Avdeev, Vasily Rozhkov and Konstantin Shugaev. It is shown that thanks to their activities, the Ekaterinburg Magnetic and Meteorological Observatory has become a reliable outpost of scientific research in the Urals. Now its successor The Ural Hydrometeorological Center has the longest range of continuous data. It is one of the key centers of global environmental monitoring as well.