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1.
Sci Rep ; 13(1): 58, 2023 Jan 02.
Artigo em Inglês | MEDLINE | ID: mdl-36593265

RESUMO

The rapid development and mutations have heightened ceramic industrialization to supply the countries' requirements worldwide. Therefore, the continuous exploration for new reserves of possible ceramic-raw materials is needed to overwhelm the increased demand for ceramic industries. In this study, the suitability assessment of potential applications for Upper Cretaceous (Santonian) clay deposits at Abu Zenima area, as raw materials in ceramic industries, was extensively performed. Remote sensing data were employed to map the Kaolinite-bearing formation as well as determine the additional occurrences of clay reserves in the studied area. In this context, ten representative clayey materials from the Matulla Formation were sampled and examined for their mineralogical, geochemical, morphological, physical, thermal, and plasticity characteristics. The mineralogical and chemical compositions of starting clay materials were examined. The physicochemical surface properties of the studied clay were studied utilizing SEM-EDX and TEM. The particle-size analysis confirmed the adequate characteristics of samples for white ceramic stoneware and ceramic tiles manufacturing. The technological and suitability properties of investigated clay deposits proved the industrial appropriateness of Abu Zenima clay as a potential ceramic raw material for various ceramic products. The existence of high kaolin reserves in the studied area with reasonable quality and quantity has regional significance. It would significantly help reduce the manufacturing cost and overwhelm the high consumption rate. The ceramic manufacturers in the investigated areas are expected to bring steady producers into the industry in the long term to gain the advantage of low-cost raw materials, labor, and factory construction.


Assuntos
Cerâmica , Tecnologia de Sensoriamento Remoto , Argila , Estudos Prospectivos , Cerâmica/química , Caulim/química
2.
Sensors (Basel) ; 23(2)2023 Jan 09.
Artigo em Inglês | MEDLINE | ID: mdl-36679564

RESUMO

In view of the fact that the aerial images of UAVs are usually taken from a top-down perspective, there are large changes in spatial resolution and small targets to be detected, and the detection method of natural scenes is not effective in detecting under the arbitrary arrangement of remote sensing image direction, which is difficult to apply to the detection demand scenario of road technology status assessment, this paper proposes a lightweight network architecture algorithm based on MobileNetv3-YOLOv5s (MR-YOLO). First, the MobileNetv3 structure is introduced to replace part of the backbone network of YOLOv5s for feature extraction so as to reduce the network model size and computation and improve the detection speed of the target; meanwhile, the CSPNet cross-stage local network is introduced to ensure the accuracy while reducing the computation. The focal loss function is improved to improve the localization accuracy while increasing the speed of the bounding box regression. Finally, by improving the YOLOv5 target detection network from the prior frame design and the bounding box regression formula, the rotation angle method is added to make it suitable for the detection demand scenario of road technology status assessment. After a large number of algorithm comparisons and data ablation experiments, the feasibility of the algorithm was verified on the Xinjiang Altay highway dataset, and the accuracy of the MR-YOLO algorithm was as high as 91.1%, the average accuracy was as high as 92.4%, and the detection speed reached 96.8 FPS. Compared with YOLOv5s, the p-value and mAP values of the proposed algorithm were effectively improved. It can be seen that the proposed algorithm improves the detection accuracy and detection speed while greatly reducing the number of model parameters and computation.


Assuntos
Algoritmos , Tecnologia de Sensoriamento Remoto , Reconhecimento Psicológico , Rotação , Coluna Vertebral
3.
Sensors (Basel) ; 23(2)2023 Jan 10.
Artigo em Inglês | MEDLINE | ID: mdl-36679569

RESUMO

As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods.


Assuntos
Redes Neurais de Computação , Tecnologia de Sensoriamento Remoto , Tecnologia de Sensoriamento Remoto/métodos , Benchmarking , Inteligência
4.
Environ Monit Assess ; 195(2): 320, 2023 Jan 23.
Artigo em Inglês | MEDLINE | ID: mdl-36689091

RESUMO

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.


Assuntos
Militares , Tecnologia de Sensoriamento Remoto , Humanos , Tecnologia de Sensoriamento Remoto/métodos , Monitoramento Ambiental/métodos , Imagens de Satélites , Algoritmos
5.
Water Res ; 230: 119540, 2023 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-36608522

RESUMO

The pollution or eutrophication affected by dissolved organic matter (DOM) composition and sources of inland waters had attracted concerns from the public and government in China. Combined with remote sensing techniques, the fluorescent DOM (FDOM) parameters accounted for the important part of optical constituent as chromophoric dissolved organic matter (CDOM) was a useful tool to trace relative DOM sources and assess the trophic states for large-scale regions comprehensively and timely. Here, the objective of this research is to calibrate and validate a general model based on Landsat 8 OLI product embedded in Google Earth Engine (GEE) for deriving humification index (HIX) based on EEMs in lakes across China. The Landsat surface reflectance was matched with 1150 pairs fieldtrip samples and the nine sensitive spectral variables with good correlation with HIX were selected as the inputs in machine learning methods. The calibration of XGBoost model (R2 = 0.86, RMSE = 0.29) outperformed other models. Our results indicated that the entire dataset of HIX has a strong association with Landsat reflectance, yielding low root mean square error between measured and predicted HIX (R2 = 0.81, RMSE = 0.42) for lakes in China. Finally, the optimal XGBoost model was used to calculate the spatial distribution of HIX of 2015 and 2020 in typical lakes selected from the Report on the State of the Ecology and Environment in China. The significant decreasing of HIX from 2015 to 2020 with trophic states showed positive control of humification level of lakes based on the published document of Action plan for prevention and control of water pollution in 2015 of China. The calibrated model would greatly facilitate FDOM monitoring in lakes, and provide indicators for relative DOM sources to evaluate the impact of water protection measures or human disturbance effect from Covid-19 lockdown, and offer the government supervision to improve the water quality management for lake ecosystems.


Assuntos
COVID-19 , Monitoramento Ambiental , Humanos , Monitoramento Ambiental/métodos , Lagos , Tecnologia de Sensoriamento Remoto , Matéria Orgânica Dissolvida , Ecossistema , Controle de Doenças Transmissíveis , China
6.
Sensors (Basel) ; 23(2)2023 Jan 06.
Artigo em Inglês | MEDLINE | ID: mdl-36679457

RESUMO

Nature reserves are among the most bio-diverse regions worldwide, and rapid and accurate identification is a requisite for their management. Based on the multi-temporal Sentinel-2 dataset, this study presents three multi-temporal modified vegetation indices (the multi-temporal modified normalized difference Quercus wutaishanica index (MTM-NDQI), the multi-temporal modified difference scrub grass index (MTM-DSI), and the multi-temporal modified ratio shaw index (MTM-RSI)) to improve the classification accuracy of the remote sensing of vegetation in the Lingkong Mountain Nature Reserve of China (LMNR). These three indices integrate the advantages of both the typical vegetation indices and the multi-temporal remote sensing data. By using the proposed indices with a uni-temporal modified vegetation index (the uni-temporal modified difference pine-oak mixed forest index (UTM-DMI)) and typical vegetation indices (e.g., the ratio vegetation index (RVI), the difference vegetation index (DVI), and the normalized difference vegetation index (NDVI)), an optimal feature set is obtained that includes the NDVI of December, the NDVI of April, and the UTM-DMI, MTM-NDQI, MTM-DSI, and MTM-RSI. The overall accuracy (OA) of the random forest classification (98.41%) and Kappa coefficient of the optimal feature set (0.98) were higher than those of the time series NDVI (OA = 96.03%, Kappa = 0.95), the time series RVI (OA = 95.56%, Kappa = 0.95), and the time series DVI (OA = 91.27%, Kappa = 0.90). The OAs of the rapid classification and the Kappa coefficient of the knowledge decision tree based on the optimal feature set were 95.56% and 0.95, respectively. Meanwhile, only three of the seven vegetation types were omitted or misclassified slightly. Overall, the proposed vegetation indices have advantages in identifying the vegetation types in protected areas.


Assuntos
Poaceae , Tecnologia de Sensoriamento Remoto , China , Monitoramento Ambiental
7.
Sensors (Basel) ; 23(2)2023 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-36679763

RESUMO

Epilepsy is a debilitating neurological condition characterized by intermittent paroxysmal states called fits or seizures. Especially, the major motor seizures of a convulsive nature, such as tonic-clonic seizures, can cause aggravating consequences. Timely alerting for these convulsive epileptic states can therefore prevent numerous complications, during, or following the fit. Based on our previous research, a non-contact method using automated video camera observation and optical flow analysis underwent field trials in clinical settings. Here, we propose a novel adaptive learning paradigm for optimization of the seizure detection algorithm in each individual application. The main objective of the study was to minimize the false detection rate while avoiding undetected seizures. The system continuously updated detection parameters retrospectively using the data from the generated alerts. The system can be used under supervision or, alternatively, through autonomous validation of the alerts. In the latter case, the system achieved self-adaptive, unsupervised learning functionality. The method showed improvement of the detector performance due to the learning algorithm. This functionality provided a personalized seizure alerting device that adapted to the specific patient and environment. The system can operate in a fully automated mode, still allowing human observer to monitor and override the decision process while the algorithm provides suggestions as an expert system.


Assuntos
Epilepsia Tônico-Clônica , Epilepsia , Humanos , Estudos Retrospectivos , Tecnologia de Sensoriamento Remoto , Eletroencefalografia/métodos , Convulsões/diagnóstico , Epilepsia/diagnóstico , Algoritmos
8.
PLoS One ; 18(1): e0279097, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36662763

RESUMO

Deeplabv3+ currently is the most representative semantic segmentation model. However, Deeplabv3+ tends to ignore targets of small size and usually fails to identify precise segmentation boundaries in the UAV remote sensing image segmentation task. To handle these problems, this paper proposes a semantic segmentation algorithm of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3+ (EMNet). EMNet uses MobileNetV2 as its backbone and adds an edge detection branch in the encoder to provide edge information for semantic segmentation. In the decoder, a multi-level upsampling method is designed to retain high-level semantic information (e.g., the target's location and boundary information). The experimental results show that the mIoU and mPA of EMNet improved over Deeplabv3+ by 7.11% and 6.93% on the dataset UAVid, and by 0.52% and 0.22% on the dataset ISPRS Vaihingen.


Assuntos
Tecnologia de Sensoriamento Remoto , Semântica , Algoritmos , Coluna Vertebral , Processamento de Imagem Assistida por Computador
9.
Sci Rep ; 13(1): 1057, 2023 Jan 19.
Artigo em Inglês | MEDLINE | ID: mdl-36658205

RESUMO

The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002-2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of - 0.80, - 0.58, - 0.43, and - 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of - 0.68, - 0.23, and - 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA-Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods.


Assuntos
Mudança Climática , Tecnologia de Sensoriamento Remoto , Humanos , Solo , Agricultura/métodos , Segurança Alimentar
10.
AAPS J ; 25(1): 13, 2023 01 10.
Artigo em Inglês | MEDLINE | ID: mdl-36627500

RESUMO

Organic anion transporter 1 (OAT1) expressed in the kidney plays an important role in the elimination of numerous anionic drugs used in the clinic. We report here that insulin, a pancreas-secreted hormone, regulated the expression and activity of kidney-specific OAT1 both in cultured cells and in rats. We showed that treatment of OAT1-expressing cells with insulin led to an increase in OAT1 expression, transport activity, and SUMOylation. Such insulin-induced increase was blocked by afuresertib, a specific inhibitor for protein kinase B (PKB), suggesting insulin regulates OAT1 through PKB signaling pathway. Furthermore, insulin stimulated transport activity and SUMOylation of endogenously expressed OAT1 in rat kidneys. In conclusion, our data support a remote sensing and signaling model, in which OAT1 plays an essential role in intercellular and inter-organ communication and in maintaining local and whole-body homeostasis. Such complex and dedicated communication is carried out by insulin, and PKB signaling and membrane sorting.


Assuntos
Insulina , Proteína 1 Transportadora de Ânions Orgânicos , Ratos , Animais , Insulina/metabolismo , Proteína 1 Transportadora de Ânions Orgânicos/metabolismo , Tecnologia de Sensoriamento Remoto , Rim/metabolismo , Transdução de Sinais , Hormônios Pancreáticos/metabolismo , Insulina Regular Humana , Transportadores de Ânions Orgânicos Sódio-Independentes/metabolismo
11.
Environ Monit Assess ; 195(2): 347, 2023 Jan 31.
Artigo em Inglês | MEDLINE | ID: mdl-36717471

RESUMO

Production plantation forestry has many economic benefits but can also have negative environmental impacts such as the spreading of invasive pines to native forest habitats. Monitoring forest for the presence of invasive pines helps with the management of this issue. However, detection of vegetation change over a large time period is difficult due to changes in image quality and sensor types, and by the spectral similarity of evergreen species and frequent cloud cover in the study area. The costs of high-resolution images are also prohibitive for routine monitoring in resource-constrained countries. This research investigated the use of remote sensing to identify the spread of Pinus caribaea over a 21-year period (2000 to 2021) in Belihuloya, Sri Lanka, using Landsat images. It applied a range of techniques to produce cloud free images, extract vegetation features, and improve vegetation classification accuracy, followed by the use of Geographical Information System to spatially analyze the spread of invasive pines. The results showed most invading pines were found within 100 m of the pine plantations' borders where broadleaved forests and grasslands are vulnerable to invasion. However, the extent of invasive pine had an overall decline of 4 ha over the 21 years. The study confirmed that remote sensing combined with spatial analysis are effective tools for monitoring invasive pines in countries with limited resources. This study also provides information to conservationists and forest managers to conduct strategic planning for sustainable forest management and conservation in Sri Lanka.


Assuntos
Pinus , Tecnologia de Sensoriamento Remoto , Tecnologia de Sensoriamento Remoto/métodos , Sri Lanka , Conservação dos Recursos Naturais/métodos , Monitoramento Ambiental/métodos , Ecossistema
12.
Sensors (Basel) ; 23(1)2023 Jan 02.
Artigo em Inglês | MEDLINE | ID: mdl-36617083

RESUMO

In recent years, the use of remotely sensed and on-ground observations of crop fields, in conjunction with machine learning techniques, has led to highly accurate crop yield estimations. In this work, we propose to further improve the yield prediction task by using Convolutional Neural Networks (CNNs) given their unique ability to exploit the spatial information of small regions of the field. We present a novel CNN architecture called Hyper3DNetReg that takes in a multi-channel input raster and, unlike previous approaches, outputs a two-dimensional raster, where each output pixel represents the predicted yield value of the corresponding input pixel. Our proposed method then generates a yield prediction map by aggregating the overlapping yield prediction patches obtained throughout the field. Our data consist of a set of eight rasterized remotely-sensed features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), aspect, and two radar backscatter coefficients acquired from the Sentinel-1 satellites. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present leave-one-out cross-validation experiments for rain-fed winter wheat over four fields and show that our proposed methodology produces better predictions than five compared methods, including Bayesian multiple linear regression, standard multiple linear regression, random forest, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.


Assuntos
Tecnologia de Sensoriamento Remoto , Triticum , Estações do Ano , Teorema de Bayes , Redes Neurais de Computação
13.
J Environ Sci (China) ; 123: 3-14, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36521992

RESUMO

Currently, the three-dimensional (3D) distribution and characteristics of air pollution cannot be understood based on the application of any single atmospheric monitoring technology. Long-term, high-precision and large-scale 3D atmospheric monitoring might become practical by combining heterogeneous modern technologies; for this purpose, the Space-Air-Ground integrated system is a promising concept. In this system, optical remote sensing technologies employing fixed or mobile platforms are used as the main means for ground-based observations. Tethered balloons, unmanned aerial vehicles (UAV) and airborne platforms serve as the air-based observation segment. The final part, satellite remote sensing, corresponds to space-based observations. Aside from obtaining the 3D distribution of air pollution, research on emission estimation and pollution mechanisms has been extensively implemented based on the strengths of this system or some portion of it. Moreover, further research on the fusion of multi-source data, optimization of inversion algorithms, and coupling with atmospheric models is of great importance to the realization of this system.


Assuntos
Poluição do Ar , Tecnologia de Sensoriamento Remoto , Tecnologia de Sensoriamento Remoto/métodos , Aeronaves
14.
J Environ Sci (China) ; 123: 317-326, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36521995

RESUMO

In recent years, with rapid increases in the number of vehicles in China, the contribution of vehicle exhaust emissions to air pollution has become increasingly prominent. To achieve the precise control of emissions, on-road remote sensing (RS) technology has been developed and applied for law enforcement and supervision. However, data quality is still an existing issue affecting the development and application of RS. In this study, the RS data from a cross-road RS system used at a single site (from 2012 to 2015) were collected, the data screening process was reviewed, the issues with data quality were summarized, a new method of data screening and calibration was proposed, and the effectiveness of the improved data quality control methods was finally evaluated. The results showed that this method reduces the skewness and kurtosis of the data distribution by up to nearly 67%, which restores the actual characteristics of exhaust diffusion and is conducive to the identification of actual clean and high-emission vehicles. The annual variability of emission factors of nitric oxide decreases by 60% - on average - eliminating the annual drift of fleet emissions and improving data reliability.


Assuntos
Poluentes Atmosféricos , Tecnologia de Sensoriamento Remoto , Poluentes Atmosféricos/análise , Calibragem , Confiabilidade dos Dados , Reprodutibilidade dos Testes , Monitoramento Ambiental/métodos , Emissões de Veículos/análise , Veículos Automotores
15.
J Environ Manage ; 325(Pt B): 116533, 2023 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-36308957

RESUMO

Urban ecological environment is the basis of citizens' survival and development. A rapid and objective urban ecological environment assessment (UEEA) plays an important role in the urban sustainable development and environment protection. This study established an improved urban ecological comfort index (UECIIMP), which is based on our previous UECI and fully composed of four remote sensing indicators: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), land surface temperature (LST), and aerosol optical depth (AOD), representing the greenness, dryness, heat, and atmospheric turbidity, respectively. Combining the entropy method and random forest (RF) algorithm, the weights of four indicators were calculated. To improve the accuracy of UECIIMP, the gap-filled quarterly mean results of each indicator with 30m resolution were obtained using the harmonic analysis of time series (HANTS) method and spatial-temporal information fusion based on non-local means filter (STNLFFM). UECIIMP was applied to the Hefei-Nanjing-Hangzhou Region to explore its spatiotemporal changes and response characteristics. Results show that the weights of UECIIMP fluctuate slightly (within 10%) before and after sensitivity analysis, with good stability and reliability. UECIIMP in Hangzhou > Hefei ≈ Nanjing, spring ≈ autumn > summer â‰« winter. From 2009 to 2019, UECIIMP has improved in all 33 districts of the Hefei-Nanjing-Hangzhou Region. The significant improvement of UECIIMP in 2014-2019 is 4.3 times than that in 2009-2014. The correlation between UECIIMP and economic index indicates that economic development has a positive impact on the urban ecological environment. The significant degradation of UECIIMP in the urban expansion area demonstrates a negative impact on the local environment from urban expansion.


Assuntos
Monitoramento Ambiental , Tecnologia de Sensoriamento Remoto , China , Cidades , Entropia , Monitoramento Ambiental/métodos , Aprendizado de Máquina , Reprodutibilidade dos Testes
16.
Water Res ; 229: 119478, 2023 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-36527868

RESUMO

Dissolved Organic Carbon (DOC) in inland waters plays an essential role in the global carbon cycle and has significant public health effects. Machine learning (ML) together with remote sensing has emerged as a powerful and promising combination to quantify water quality parameters from space. However, inland water sample data for DOC is limited. Hence, little is known about the potential to quantify DOC content in inland waters, especially over large-scale areas. This study presents the first attempt to estimate DOC in inland waters over a large-scale area using satellite data and ML methods with the newly published open-source dataset AquaSat. Four ML approaches, namely Random Forest Regression (RFR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and a Multilayer Backpropagation Neural Network (MBPNN) were trained using more than 16 thousand samples across the continental United States matched with satellite data from Landsat 5, 7 and 8 missions. Satellite data from the Landsat missions were further extended with environmental data from the ERA5-Land product and used as input to train the ML algorithms. Our results show that including environmental data as inputs considerably improved the prediction of DOC for all ML algorithms, with GPR showing the most promising performance results with moderate estimation errors (RMSE: 4.08 mg/L). Permutation feature importance analysis showed that the wavelength range in the visible Green band (from Landsat) and the monthly average air temperature (from ERA5-Land) were the most important variables for the ML approaches. The results demonstrate the predictive strength of GPR and its useful feature to derive per pixel standard deviations for detailed analysis. Our results further highlight the important role of considering environmental processes to explain DOC variations over large scales. The application and performance of the GPR in mapping spatiotemporal variations of DOC in an entire water body were discussed by taking Lake Okeechobee (the 8th largest freshwater lake in the U.S.) as an illustrative example. While performance evaluation showed that DOC concentrations can be retrieved with adequate accuracy, algorithm development was challenged by the heterogenous nature of large-scale open source in situ data, issues related to atmospheric correction, and the low spatial and temporal resolution of the environmental predictors. This research demonstrates how open source, large-scale datasets like AquaSat in combination with ML and satellite remote sensing can make research toward large-scale estimation of inland water DOC more realistic while highlighting its remaining limitations and challenges.


Assuntos
Matéria Orgânica Dissolvida , Qualidade da Água , Lagos , Tecnologia de Sensoriamento Remoto , Aprendizado de Máquina , Monitoramento Ambiental/métodos , Carbono
17.
Sci Total Environ ; 863: 160961, 2023 Mar 10.
Artigo em Inglês | MEDLINE | ID: mdl-36529399

RESUMO

The hydrological regime is one of the most significant characteristics of wetlands, which maintains the structural and functional integrity of wetland ecosystems. China experienced rapid economic development since the 1990s, which caused severe degradation of all types of wetlands, especially marsh wetlands that are easily converted through filling or draining. Therefore, it is crucial to examine the inundation alterations in marshes as well as the forces behind the changes. In this study, the inundation dynamics in marsh wetlands of China were documented using time-series Landsat observations from 1992 to 2018. Then, nighttime light data was utilized to indicate the intensity of urbanization and infrastructure construction, which was incorporated with historical statistics to conduct attribution analyses of wetland inundation changes. Great spatial heterogeneity in the water distribution and change trajectory was observed in different areas. Severe wetland desiccation took place in Inner Mongolia and East China, in which the inundation area decreased by 51.3 % and 20.9 %, respectively. By contrast, the water area in North China and Tibetan Plateau increased by 58.2 % and 21.0 %, respectively. Behind the tremendous changes, anthropogenic factors played dominant roles. The marsh wetlands in East China, North China, and Southwest China took up only 1.9 % of the total marsh area but accounted for 26.0 % of the entire nighttime light volume. In East China and Southwest China, urbanization and infrastructure construction had significantly negative effects on wetland inundation. Overgrazing or unregulated irrigation altered the original inundation dynamics of marsh wetlands in Inner Mongolia, Southwest China, the Tibetan plateau, and Northeast China. This study illustrated the possible driving forces behind wetland inundation changes, which could help to locate degrading marsh wetlands triggered by anthropogenic activities. Then, targeted management and conservation actions could be implemented.


Assuntos
Ecossistema , Áreas Alagadas , Humanos , Tecnologia de Sensoriamento Remoto , Fatores de Tempo , China , Água
18.
Sci Total Environ ; 864: 161135, 2023 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-36566867

RESUMO

The over-exploitation of mineral resources has led to increasingly serious dust pollution in mines, resulting in a series of negative impacts on the environment, mine workers (occupational health) and nearby residents (public health). For the environment, mine dust pollution is considered a major threat on surface vegetation, landscapes, weather conditions and air quality, leading to serious environmental damage such as vegetation reduction and air pollution; for occupational health, mine dust from the mining process is also regarded as a major threat to mine workers' health, leading to occupational diseases such as pneumoconiosis and silicosis; for public health, the pollutants contained in mine dust may pollute surrounding rivers, farmlands and crops, which poses a serious risk to the domestic water and food security of nearby residents who are also susceptible to respiratory diseases from exposure to mine dust. Therefore, the second section of this paper combines literature research, statistical studies, and meta analysis to introduce the public mainly to the severity of mine dust pollution and its hazards to the environment, mine workers (occupational health), and residents (public health), as well as to present an outlook on the management of mine dust pollution. At the same time, in order to propose a method for monitoring mine dust pollution on a regional scale, based on the Dense Dark Vegetation (DDV) algorithm, the third section of this paper analysed the aerosol optical depth (AOD) change in Dexing City of China using the data of 2010, 2014, 2018 and 2021 from the NASA MCD19A2 Dataset to explore the mine dust pollution situation and the progress of pollution treatment in Dexing City from 2010 to 2021. As a discussion article, this paper aims to review the environmental and health risks caused by mine dust pollution, to remind the public to take mine dust pollution seriously, and to propose the use of remote sensing technologies to monitor mine dust pollution, providing suggestions for local governments as well as mines on mine dust monitoring measures.


Assuntos
Poluição do Ar , Exposição Ocupacional , Humanos , Poeira/análise , Tecnologia de Sensoriamento Remoto , Aerossóis e Gotículas Respiratórios , Poluição Ambiental/análise , Monitoramento Ambiental/métodos
19.
Sci Total Environ ; 858(Pt 3): 160045, 2023 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-36372165

RESUMO

The construction of large dams along rivers has significantly changed the natural flow regime, reducing the inflow into many lakes and terminal wetlands. However, the question of the impact of dam operation on downstream estuarine wetlands has less been taken into account. Spatio-temporal flow regime alteration in the Mond River shows the complexity of drivers affecting the estuary-coastal system named the Mond-Protected Area in southern Iran. To this end, we applied river impact (RI) and Indicator of hydrological alteration (IHA) methods on monthly and daily river flow data across the basin. Based on the river impact method, a "drastic" impact below two in-operation (Tangab and Salman Farsi) dams, with RI values of 0.02 and 0.08, diminish to a 'severe' impact with RI value of 0.35 at the last gauge (Ghantareh) on the main corridor of the Mond river due to the addition of flow from a large mid-basin (about 20,254 km2). Furthermore, the degree of hydrological alteration (daily flow analysis) at mid-stream (e.g., Dehram gauges) was similar to the unregulated upstream tributaries (e.g., Hanifaghan gauges). The remote sensing analysis in the Mond Protected Area showed the prevailing impact of sea-level rise in the Persian Gulf with the inundation of the coastal area and a shift of vegetation in a landward direction which complied with standardized precipitation index (SPI) values as a meteorological drought indicator. Thus, the consequence of climate change (e.g., sea-level rise, draught) has a higher impact on the protected area than the upstream river regulation and land-use change in the Mond basin. The holistic approach and the catchment-level study allowed us to see the complexity of the drivers influencing the estuary-coastal system.


Assuntos
Monitoramento Ambiental , Movimentos da Água , Hidrologia , Irã (Geográfico) , Rios , Monitoramento Ambiental/métodos , Tecnologia de Sensoriamento Remoto , Estuários
20.
Environ Res ; 219: 114955, 2023 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-36495962

RESUMO

Hydrocarbon-contaminated soils are considered as one of the major environmental issues that harm human well-being, particularly in arid regions of the world. Phytoremediation is a possible mitigation measure for this issue and has been suggested as it is cost-effective compared with other remediation technologies for soil clean-up, such as soil thermal treatment and soil washing. However, there are still gaps in the literature regarding the behavior of annual and perennial desert plants and their ability to survive in hydrocarbon-contaminated soils in arid ecosystems. Therefore, this study aims to develop an integrated approach using remote sensing techniques to understand the behavior of annual and perennial desert plants over different types of oil-contaminated soils (oil tarcrete, wet-oil lake, bare soil, and vegetation cover) in the Kuwait Desert and to explore the impact of climate and physical soil properties on the regrowth of native desert plants. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and ferrous iron (Fe2+) index (FI) were used to determine the changes in oil contamination and vegetation cover from 1992 to 2002, and 2013-2020. Subsequently, statistical tests were performed to determine the influence of climatic and soil physical characteristics on changes in hydrocarbon contamination and desert plant behavior. The results showed that hydrocarbon contamination was high at the study sites in the first six years (1992-1997) after contamination, and then decreased in the following years. However, vegetation cover was low in the first six years but significantly increased after 1998, reaching >65%. It was also found that annual plants had the highest distribution rate compared to perennial plants, which mainly depended on the soil type. We concluded that certain annual and perennial plants could successfully grow over tarcrete-contaminated sites, making these sites more suitable for the restoration of native desert plants than hydrocarbon-contaminated sites. We also observed that the succession process of vegetation growth over hydrocarbon-contaminated soils could be associated with vegetation growth on a clean sediment layer covering the oil layer. Additionally, we observed that the remobilization of aeolian sediment over many contaminated sites in Kuwait resulted in the accumulation of organic matter, plant seeds, and dust particles that create layers of nutrient-rich soil for the initial growth of plants.


Assuntos
Ecossistema , Poluentes do Solo , Humanos , Tecnologia de Sensoriamento Remoto , Poluentes do Solo/análise , Solo , Plantas , Biodegradação Ambiental , Hidrocarbonetos
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